Robust Moving Object Detection at Distance in the ... - IEEE Xplore

Robust Moving Object Detection at Distance in the Visible Spectrum and Beyond Using A Moving Camera Yan Zhang, Stephen J. Kiselewich, William A. Bauson, and Riad Hammoud Delphi Electronics & Safety, Kokomo, Indiana 46904-9005

Abstract Automatic detection of moving objects at distance and in all weather conditions is a critical task in many visionbased safety applications such as video surveillance and vehicle forewarn collision warning. In such applications, prior knowledge about the object class (vehicle, pedestrian, tree, etc.) and imaging conditions (shadow, depth) is unavailable. What makes the task even more challenging is when the camera is non-stationary, e.g., mounted on a moving vehicle. The essential problem in this case lies in distinguishing between camera-induced motion and independent motion. This paper proposes a robust algorithm for automatic moving object detection at distance. The camera is mounted on a moving vehicle and operates in both day and night time. Through the utilization of the focus of expansion (FOE) and its associated residual map, the proposed method is able to detect and separate independently moving objects (IMOs) from the ”moving” background caused by the camera motion. Experimentations on numerous realworld driving videos have shown the effectiveness of the proposed technique. Moving objects such as pedestrians and vehicles up to 40 meters away from the camera have been reliably detected at 10 frames per second on a 1.8GHz PC.

1. Introduction Automatic object detection is a key module in many vision-based applications as the auto-initialization step for further vision and pattern recognition tasks such as object recognition and tracking. The success of automatic object detection algorithms depends on the application, assumptions and capabilities of the sensor. For video surveillance tasks, the imager is often stationary and therefore automatic detection of moving objects is relatively easy since the model of the background is known [21]. Background model estimation and subtraction techniques have been widely used in such cases [5]. The moving object detection task becomes more challenging when the camera

(observer) moves as well, due to the background ”motion” which is induced by the camera. Background estimation and subtraction techniques do not apply in such circumstances. As opposed to purely camera-induced motion of the otherwise stationary background, the motion that is independent of the moving camera is often referred to as ”independent motion” in the literature [9, 22]. Similarly, the objects with such motion are referred to as ”independently moving objects” (IMOs). For instance, compared to the seemingly moving ground induced by the camera motion, a moving pedestrian or vehicle is an IMO. The essential problem in detecting a moving object with a moving camera lies in distinguishing between camera-induced motion and independent motion. From now on, moving object detection in this context refers to IMO detection unless otherwise noted. We may broadly categorize object detection methods as either recognition from a previously learned class (e.g. faces) or, in a less supervised fashion, decision based on observations of salient features in the image, such as movements. The second category is more related to our work presented here. Indeed, locating mobile objects requires partitioning the scene into regions of independent motions. In practice, however, one often avoids computing the motion of these mobile objects, since this is often unreliable on a small estimation support, and one instead determines spatially disconnected regions that do not conform to the estimated background motion (if any). At this point, differences between image intensity (or, alternatively, the normal residual flow after global motion cancellation [17]) forms the basis of observations. The subsequent decision rule (mobile or background) involves learning a statistical model of the background, for instance via a mixture of Gaussians [7], to ensure robustness to noise. It is however more tricky to model intensity variability for the alternative (motion) hypothesis - see [28] for a recent analysis of the matter. An essential difficulty in this task is that motion can only be partly observed, i.e. it is, by and large, apparent only on intensity edges that are not parallel to the motion flow. Consequently, mechanisms are commonly introduced into motion detection schemes

Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) 0-7695-2646-2/06 $20.00 © 2006 IEEE

to propagate information towards areas of lower contrast, where motion remains hidden: probabilistic modeling of the image as a Markov random field with Bayesian estimation, on one side, and variational approaches, on the other side, are two popular frameworks to recover the full extent of the object. Distinguishing meaningful displacements from noise can also rely on the temporal consistency of motion: techniques for accumulating information over time in various fashions have proved effective, especially for small or slow objects [19]. One could also categorize existing object detection techniques into 2D and 3D classes according to how the depth variation in a scene compares to the absolute depth of the scene. Due to the different constraints and assumptions upon which these two types of approaches are based, they often fail when used in the other domain, i.e., 2D methods will not work for 3D scenes and vice versa. According to the comprehensive summary in [9], 2D methods handle scenes that meet one of the following criteria including (i) the scene is a flat surface, (ii) the depth variation in the scene is much smaller than the overall depth of the scene, and (iii) the camera only has rotation and zoom. While two quadratic polynomials with eight unknowns fully define the image motion field of a moving 2D scene (plane), most 2D methods estimate such parametric motion as the dominant motion of the background, i.e., the camera motion. The discrepancy in the dominant background motion indicates the presence of IMOs [10, 3]. On the other hand, most 3D methods account for the camera-induced motion either explicitly by ego-motion estimation in a more traditional way of rotation and translation separation, or implicitly using plane+parallax techniques. The main advantages of the plane+parallax techniques include the feasibility of the planar homography estimation and the avoidance of the explicit ego-motion estimation. As the residual planar parallax is only caused by the camera translation (not rotation and zoom), it forms an image motion field converging to the focus of expansion (FOE). The motion field that violates this convergence indicates the presence of IMOs [27]. Sawhney et al. [22] proposed a planar+parallax approach for independent motion detection in 3D scenes. The authors employed both epipolar constraints and shape rigidity to solve the planar and parallax parameters progressively from multiple images. Based on the assumption of a constant FOE, Hu and Uchimura [8] detected moving objects through the explicit estimation of the ego-motion from feature correspondences between two adjacent video frames. Sharma and Aloimonos [24] introduced an active mobile system where the ego-motion was controllable and known. The authors then used shifting FOEs and normal image flow to detect independent motion. Similarly, [16, 12] exploited the normal image flow field for independent motion detection. Nair and Aggarwal [15] improved the ego-motion ac-

curacy using both computer vision techniques and an odometry. Curio et al. [4] detected pedestrian hypotheses through image texture analysis and validated the objects using a mathematical model of the walking human. The model involves the moving pattern of the lower part of the human body. Takeda et al. [26, 29] presented a moving obstacle detection approach using the FOE and its residual error. Most recently, Arnell and Petersson [1] applied the u-disparity technique to image motion field and fitted the u-disparity of the camera-induced motion into a quadratic curve. They segmented IMOs of which the motion is not on the quadratic curve. The detection of moving objects in an automotive safety system requires the system to run in real time. Considering that the camera (vehicle) rotation is less frequent than its translation, an assumption on negligible camera rotation is appropriate. With such an assumption, the estimation of the planar homography and the camera rotation is avoided and the moving object detection is simplified. This paper presents a robust algorithm for detecting IMOs at distance through the utilization of the focus of expansion (FOE) and its associated residual map. The main contributions of this paper include robust estimation of the focus of expansion (FOE), FOE residual map extraction and segmentation, and the application of the proposed methods to nighttime images. Although the FOE has its roots in the literature, our proposed techniques differs in many aspects. First, we propose a robust estimation of the FOE through sparse optical flow vectors and extract the corresponding residual map. In contrast, [26] used a dense optical flow and estimated local FOEs for each sub-region and extracted the residual map. The method in [26] obtained the FOE residual map through a multi-resolution routine in order to represent various scales of the moving objects. However, we found that a single fine resolution achieved a better FOE residual map for objects at various ranges (scales). Second, we propose a further contrast enhancement step of the residual map for more efficient segmentation and a region growing based segmentation, while such steps were not addressed in [26]. We utilize a single monochrome camera system that operates in day and night time. The camera is mounted on a moving vehicle. The system has been tested on both daytime and nighttime video sequences. The experiments have demonstrated that the proposed method is able to detect moving pedestrians at up to 40 meters and 30 meters for daytime and nighttime scenarios respectively. The current system runs at 10 frames per second on a 1.8GHz PC with an Intel processor. The proposed method directly applies to other tasks such as moving pedestrian detection [30] and cut-in vehicle detection. The remainder of this paper is organized as follows. Section 2 first provides an overview of the proposed method. Section 3 presents the robust estimation of the FOE. Sec-

Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) 0-7695-2646-2/06 $20.00 © 2006 IEEE

tion 4 describes the extraction and segmentation of the FOE residual map. Section 5 presents experimental results on various daytime and nighttime video sequences. Finally, Section 6 concludes the paper.

background pixels converge to the FOE as illustrated in Fig. 2. In contrast, the objects for which the optical flow vectors do not converge to the FOE are identified as IMOs.

2. Overview of the proposed method y

The proposed method consists of five major steps including interest point detection, optical flow computation, robust estimation of FOE, FOE residual map computation and enhancement, and FOE residual map segmentation. Fig. 1 shows the diagram of the proposed approach. Interest point detection

Optical flow computation

FOE residual map extraction

FOE residual map segmentation

FOE estimation

Figure 1. Diagram of the proposed method.

Optical flow has been widely used as an approximation of the motion field in the image. An essential drawback of optical flow computation lies in its low accuracy for lowtextured regions. In addition, a dense optical flow field is computationally demanding. In order to overcome these limitations we compute optical flow vectors only at particular points in the image. These points are detected automatically using an interest point detector [23, 18, 25]. We have employed improved Harris detector [23] and detected interest points including geometric corners and intersections of image lines with high intensity gradients. The LucasKanade algorithm [13] has been exploited to compute the optical flow for these interest points due to its accuracy and robustness according to the survey in [2]. Following the optical flow computation, we propose the regression diagnostics for a robust and accurate FOE estimation. The FOE residual map is next obtained and normalized into [0, 255] followed by a contrast enhancement step. A region-growing based image segmentation segments the FOE residual image and detects IMOs as regions with FOE residuals larger than a threshold. Finally, several constraints on the detected regions such as the size of the bounding box, the aspect ratio, and the perspective constraints further reduce the false detections.

3. Robust estimation of the FOE The focus of expansion denotes the point in the image that corresponds to the intersection of the instantaneous velocity of the camera and the image plane. When the camera only has translational motion, the optical flow vectors of the

o x z

Figure 2. Optical flow vectors converge to the FOE under a camera translation.

We define a 3D coordinate system where the Z axis is parallel to the optical axis of the camera and the X-Y axes are parallel to the image plane as shown in Fig. 2. For a point P (X, Y, Z) in this 3D frame and its image projection p(x, y, z), the 2D image motion field of the point p is derived as follows under a pure camera translation in the 3D space: Tz x − Tx f Z Tz y − Ty f , vy = Z vx =

(1)

where Tx , Ty , Tz are the 3D translational components and f is the focal length of the camera. By defining the FOE as f Tx Tz f Ty y0 = , Tz x0 =

(2)

Eq. 1 becomes Tz Z Tz vy = (y − y0 ) . Z vx = (x − x0 )

(3)

We further formulate Eq. 3 into vy x0 − vx y0 + vx y − vy x = 0, and ⎛

vy1 ⎜ vy2 ⎜ ⎜ .. ⎝ . vyN

⎞ ⎛ −vx1 vy1 x1  ⎜ vy2 x2 −vx2 ⎟ ⎟ x0 ⎜ =⎜ . ⎟ ⎠ y0 ⎝ .. −vxN vyN xN

Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) 0-7695-2646-2/06 $20.00 © 2006 IEEE

(4)

⎞ −vx1 y1 −vx2 y2 ⎟ ⎟ ⎟ . (5) ⎠ −vxN yN

which is a linear system with two unknowns of the FOE (x0 , y0 ). To solve this linear least squares problem, we have chosen the singular value decomposition (SVD) over the direct method based on normal equations due to the robustness to roundoff errors and the reliable performance of the SVD [20]. Although the SVD method provides a more accurate and reliable solution to the linear system than the direct method, it lacks the capability of handling outliers in the data set. Since the FOE is related to the relative motion between the viewing camera and the stationary background, the outliers in the data originate from three sources in the scope of the FOE estimation including optical flow errors, IMOs, and the camera rotation. These outliers often cause bias to the estimated FOE. Compared to the more complicated and time-consuming robust estimators such as M-estimators and least-median-of-squares (LmedS) methods [14], the regression diagnostics [31] is sufficient for our application where the outliers are only a small portion of the entire data set and do not deviate too much from the remaining good data. The regression diagnostics aims at iteratively detecting possible outliers and reject them through an analysis of the residuals in the fitted model. We have improved the standard regression diagnostics routine by incorporating an adaptive threshold. The improved regression diagnostics is an iterative process and proceeds as follows. First, an initial fit to the entire data set is obtained by solving the linear least-squares problem using the SVD. Second, the residual for each datum is computed according to Eqs. 4 and 5, and the average residual is obtained consequently. We set the residual threshold T adaptively as C times the average residual, where the constant C is a user-specified parameter. Third, the data for which the residual exceeds the residual threshold T are rejected. Finally, a new FOE is estimated from the pruned data with fewer outliers and a smaller average residual. The iteration terminates when one of the following three criteria is fulfilled: there is no outlier to be removed according to the residual threshold; the difference of the FOE location between two iterations is less than one pixel; or the number of iterations reaches a user-specified maximum.

4. FOE residual map extraction and segmentation Once the FOE is obtained, we can compute the FOE residual for each interest point and identify the points with large residuals. However, in order to perform further tasks such as object classification and tracking, we often need IMO regions and therefore a FOE residual map for all pixels in the image. IMOs composed of sparse points are insufficient for such tasks. For a M × M window in the image, the residual of each pixel in this region is set to the average

residual of the interest points in this region. The final residual map is obtained by shifting this window by k pixels horizontally and vertically and accumulating the residuals for each pixel in this region. A single resolution of M = 32 and k = 8 yields satisfying results for most images containing objects in various ranges and scales. After the FOE residual map for all pixels in the entire image has been obtained, a linear scaling process normalizes the residual map into [0, 255] resembling a 8-bit grayscale image. Next, a linear contrast enhancement step further enhances the contrast of the residual image followed by an image thresholding. A morphological closing operator fuses small breaks and eliminates small holes in the binarized residual image. A region-growing segmentation approach based on connected-components analysis segments the binary residual image and detects regions with large residuals. Finally, several constraints on the detected regions such as the size of the bounding box, the aspect ratio, and the perspective constraints regarding the relative position of the lower boundary of the region to the y coordinate of the FOE further reduce the false detections. In particular, the last constraint indicates that the lower boundary of the detected region cannot be above the FOE based on the fact that the moving objects must be on the ground. We set a tolerance of several pixels to account for the FOE accuracy. This constraint is very efficient in eliminating false detections caused by trees and poles. After the moving objects are detected, feature extraction and classification techniques can be added for more specific object classification tasks such as pedestrian or vehicle classification. Such object classification can also further eliminate false detections. Fig. 3 shows the FOE residual image and detected IMOs from two consecutive video frames. Both the moving vehicle and the walking pedestrian have been detected successfully. Figs. 3(a) and 3(b) show the two consecutive video frames. The magnitude of the optical flow vectors shown in Fig. 3(c) have been amplified by six times for display. Fig. 3(d) shows the normalized FOE residual image. The residual image indicates that the independent motion has yielded larger residuals than the camera-induced motion in the background. Figs. 3(e) and 3(f) show the segmented residual image and detected IMOs. The green bounding boxes and the red dot in Fig. 3(f) represent detected IMOs and the FOE.

5. Experimental results The proposed algorithm has been implemented on a 1.8GHz PC. It runs at 10 frames per second. Experimentations have been conducted on road at day and night time. The imager is sensitive to near infrared light of which the wavelength is around 850nm. During these experiments the speed of the host vehicle was between 30 and 40 miles an hour. We have evaluated the minimum size of the object our

Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) 0-7695-2646-2/06 $20.00 © 2006 IEEE

(a)

(d)

(b)

(e)

(c) (a)

(b)

(c)

(d)

(f)

Figure 3. FOE residual image and IMO detection. (a) Frame k, (b) frame k + 1, (c) the optical flow for the interest points, (d) the normalized FOE residual image, (e) the segmented residual image, and (f) the detected IMOs.

25

Average FOE residual

20

system is able to detect in an image. This variable is critical and determines the largest distance at which our system can detect a moving object. For daytime scenarios, we have been able to detect moving pedestrians of 12 × 29 pixels reliably. At such pixel resolution, a 1.75m-tall person corresponds to 38m away from the camera. For nighttime scenarios, we are able to detect moving pedestrians reliably up to 26m away. The following subsections present experimental results for daytime and nighttime IMO detection and the application of our system to cut-in vehicle detection. Fig. 4 illustrates the robust FOE estimation for the two consecutive frames shown in Figs. 4(a) and 4(b). Fig. 4(c) depicts the computed optical flow. Fig. 4(d) indicates that most outliers have been detected on the laterally moving vehicle as expected. Fig. 4(e) shows that the initial estimation of the FOE (the blue dot) is biased to the left caused by the IMO while the robust FOE (the red dot) is more accurate and consistent to the moving direction of the host vehicle. Fig. 4(f) illustrates that the residual decreased monotonically during the regression diagnostics as the FOE converged to an optimal estimation after six iterations. The final FOE converged to (146, 77) from the initial estimation of (122, 86) in the image frame of which the upper left corner is the origin, and the x and y axis points to the right and downward, respectively. The corresponding average residual decreased from 22.59 to 4.15 during the regression diagnostics. Based on the experiments on 30 video sequences composed of more than 3000 frames, we observed that the proposed method was able to detect IMOs of 12 × 29 pixels reliably. As an initial object detection step for object recognition or tracking tasks, the detection rate is more important than the false detection at this stage. A classification step can always be employed to effectively reduce false detec-

15

10

5

0 1

2

3

4

5

6

7

8

9

10

Iteration

(e)

(f)

Figure 4. Robust estimation of the FOE. (a) Frame k, (b) frame k + 1, (c) the optical flow vectors, (d) pixels with large FOE residuals plotted in green, (e) blue dot: initial FOE, red dot: robust FOE, and (f) the monotonically decreasing FOE residual during the regression diagnostics.

tions. Therefore, we have adjusted the parameters in the algorithm to achieve a detection rate of 100% for objects of 12 × 29 pixels or larger while still keeping the false detection rate resonally low. We also tested scenarios where the host vehicle (camera) was turning. The experimental results demonstrated that the proposed technique is able to handle camera rotations to some extent. And the correct moving object detection does not rely on a perfect FOE estimation. Fig. 5 shows the IMO detection for a scene containing independently moving vehicles. Figs. 5(a) and 5(b) are two adjacent frames in the video sequence. Fig. 5(c) depicts the normalized FOE residual map where the IMOs apparantly have larger residuals than the background. Fig. 5(d) shows the estimated FOE as a red dot and detected moving vehicles in green bounding boxes. We observe that all three laterally moving vehicles have been detected correctly. Fig. 6 presents the IMO detection for a scene containing a walking pedestrian. Figs. 6(a) and 6(b) represent two adjacent frames in that video sequence. Fig. 6(c) represents

Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) 0-7695-2646-2/06 $20.00 © 2006 IEEE

(a)

(b)

(a)

(b)

(c)

(d)

(c)

(d)

Figure 5. IMO detection: moving vehicles. (a) Frame k, (b) frame k + 1, (c) the FOE residual image, and (d) the detected IMOs.

the residual image. As can be seen, the FOE is approximately 5-pixel below the ideal location caused by vehicle rotations (road bumps, etc.). However, the walking pedestrian still has prominent residuals as shown in the residual image. Fig. 6(d) depicts the estimated FOE in a red dot and the detected walking pedestrian in bounding boxes. Although some areas in the background have large residuals in the residual image as shown in Fig. 6(c), the false detection has been successfully suppressed by the constraints described in Section 4. The walking pedestrian has been detected correctly despite the slight vehicle rotation. The experiment demonstrates that the correct moving object detection does not rely on a perfect FOE estimation. Fig. 7 shows the detection of a moving cyclist when the host vehicle was slowly turning left. Figs. 7(a) and 7(b) represent two adjacent frames in the video sequence. Fig. 7(c) shows the residual image. Fig. 7(d) depicts the estimated FOE and the detected IMOs. Both the laterally moving vehicle and the cyclist have been detected correctly despite the slight rotation of the camera (host vehicle). While only 28% of driving is conducted at night, over 62% of pedestrian fatalities occur at night. Nighttime object detection is an important but challenging task due to the low visibility and contrast. Most existing pedestrian detection systems for nighttime exploited thermal imagers [11, 6] and the high contrast between the heat-emitting pedestrians and the passive background in the thermal image. To achieve a high performance/cost ratio, we use the same video camera as in daytime IMO detection for nighttime scenarios with appropriate near-infrared illumination. The experiments have demonstrated that the proposed method is

Figure 6. IMO detection: walking pedestrians. (a) Frame k, (b) frame k + 1, (c) the FOE residual image, and (d) the detected walking pedestrian.

(a)

(b)

(c)

(d)

Figure 7. IMO detection: moving vehicles and cyclists. (a) Frame k, (b) frame k+1, (c) the FOE residual image, and (d) the detected IMOs.

able to reliably detect walking pedestrians at up to 26m for nighttime scenarios. Fig. 8 presents nighttime IMO detection from a nearinfrared video sequence containing two walking pedestrians. Figs. 8(c) and 8(f) illustrate that both walking pedestrians have been correctly detected at various distances. Fig. 9 shows another example of nighttime IMO detection in a shopping plaza. For the two adjacent frames shown

Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) 0-7695-2646-2/06 $20.00 © 2006 IEEE

(a)

(b)

(c)

(d)

(e)

(f)

Figure 8. Nighttime IMO detection. (a) Frame k, (b) frame k + 1, (c) the detected IMOs, (d) frame m, (e) frame m + 1, and (f) the detected IMOs.

in Figs. 9(a) and 9(b), all the moving pedestrians and vehicles have been correctly detected as indicated in Fig. 9(c).

(a)

(b)

(c)

Figure 9. An example of IMO detection. (a) Frame k, (b) frame k + 1, and (c) the detected IMOs.

The proposed method also applies to cut-in vehicle detection. Fig. 10 provides such an example.

(a)

(b)

(c)

Figure 10. IMO detection applied to cut-in vehicle detection. (a) The detected cut-in vehicle in frame k, (b) frame k + 1, and (c) frame k + 2.

6. Conclusion This paper tackled a challenging problem in computer vision: automatic detection of moving objects at low resolutions using a non-stationary imager that operates in and beyond the visible spectrum. First, we have proposed a robust algorithm to estimate the FOE through sparse optical flow vectors. A subsequent segmentation of the FOE

residual map separates the independly moving objects from the camera-induced motion. This algorithm is now integrated in our Delphi safety vehicle prototype and runs in near real-time. We have tested the system on both daytime and nighttime image sequences. The experiments demonstrate that the proposed method is able to reliably detect moving pedestrians at up to 38 and 26 meters, respectively, for daytime and nighttime scenarios. Most false positives occur on trees and poles along the roadside and can be easily eliminated through a subsequent classification process.

References [1] F. Arnell and L. Petersson. Fast object segmentation from a moving camera. In Proc. IEEE Intell. Veh. Symposium, volume 4, pages 136–141, 2005. 2 [2] J. L. Barron, D. J. Fleet, and S. S. Beauchemin. Systems and experiments performance of optical flow techniques. International Journal of Computer Vision, 12(1):43–77, 1994. 3 [3] J. R. Bergen, P. J. Burt, R. Hingorani, and S. Peleg. A three frame algorithm for estimating two-component image motion. IEEE Trans. Pattern Anal. Machine Intell., 14(9):886– 896, 1992. 2 [4] C. Curio, J. Edelbrunner, T. Kalinke, C. Tzomakas, and W. von Seelen. Walking pedestrian recognition. IEEE Trans. Intell. Transport. Syst., 1(3):155–163, 2000. 2 [5] J. Davis and V. Sharma. Fusion-based backgroundsubtraction using contour saliency. In Proc. IEEE CVPR Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum, pages 155–163, 2005. 1 [6] Y. Fang, K. Yamada, Y. Ninomiya, B. K. P. Horn, and I. Masaki. A shape-independent method for pedestrian detection with far-infrared images. IEEE Trans. Veh. Technol., 53(6):1679–1697, 2004. 6 [7] W. Grimson, L. Lee, R. Romano, and C. Stauffer. Using adaptive tracking to classify and monitor activities in a site. In Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, 1998. 1 [8] Z. Hu and K. Uchimura. Moving object detection from time-varied background: an application of camera 3D motion analysis. In Proc. IEEE Int’l Conf. 3-D Imaging and Modeling, pages 59–67, 1999. 2 [9] M. Irani and P. Anandan. A unified approach to moving object detection in 2D and 3D scenes. IEEE Trans. Pattern Anal. Machine Intell., 20(6):577–589, 1998. 1, 2 [10] M. Irani, B. Rousso, and S. Peleg. Computing occluding and transparent motions. International Journal of Computer Vision, 12:5–16, 1994. 2 [11] X. Liu and K. Fujimura. Pedestrian detection using stereo night vision. IEEE Trans. Veh. Technol., 53(6):1657–1665, 2004. 6 [12] M. I. A. Lourakis, A. A. Argyros, and S. C. Orphanoudakis. Independent 3d motion detection using residual parallax normal flow fields. In Proc. IEEE Int’l Conf. Computer Vision, pages 1012–1017, 1998. 2

Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) 0-7695-2646-2/06 $20.00 © 2006 IEEE

[13] B. D. Lucas. Generalized image matching by the method of differences. PhD thesis, Dept. of computer Science, Carnegie-Mellon University, 1984. 3 [14] P. Meer, D. Mintz, and A. Rosenfeld. robust regression methods for computer vision: a review. International Journal of Computer Vision, 6(1):59–70, 1991. 4 [15] D. Nair and J. K. Aggarwal. Moving obstacle detection from a navigating robot. IEEE Trans. Robot. Automat., 14(3):404– 416, 1998. 2 [16] P. Nordlund and T. Uhlin. Closing the loop: detection and pursuit of a moving object by a moving observer. Proc. IEEE Intell. Veh. Symposium, 14:265–275, 1996. 2 [17] J.-M. Odobez and P. Bouthemy. Separation of moving regions from background in an image sequence acquired with a mobile camera. In Video Data Compression for Multimedia Computing, pages 283–311. Kluwer Academic Publisher, 1997. 1 [18] G. Olague and B. Hernandez. A new accurate and flexible model based multi-corner detector for measurement and recognition. Pattern Recognition Letters, (26):27–41, 2005. 3 [19] R. Pless, T. Brodsky, and Y. Aloimonos. Detecting independent motion : the statistics of temporal continuity. IEEE Trans. Pattern Anal. Machine Intell., 22(8):768–777, 2000. 2 [20] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical recipes in C: the art of scientific computing. Cambridge University Press, 1996. 4 [21] C. Rother, V. Kolmogorov, and A. Blake. Grabcut - interactive foreground extraction using iterated graph cuts. ACM Trans. Graphics, 23(3):309–314, Aug. 2004. 1 [22] H. S. Sawhney, Y. Guo, and R. Kumar. Independent motion detection in 3D scenes. IEEE Trans. Pattern Anal. Machine Intell., 22(10):1191–1199, 2000. 1, 2 [23] C. Schmid, R. Mohr, and C. Bauckhage. Evaluation of interest point detectors. International Journal of Computer Vision, 37(2):151–172, 2000. 3 [24] R. Sharma and Y. Aloimonos. Early detection of independent motion from active control of normal image flow patterns. IEEE Trans. Syst., Man, Cybern. B, 26(1):42–52, 1996. 2 [25] J. Shi and C. Tomasi. Good features to track. In Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pages 593–600, 1994. 3 [26] N. Takeda, M. Watanabe, and K. Onoguchi. Moving obstacle detection using residual error of FOE estimation. In Proc. IEEE Int’l Conf. Intelligent Robots and Systems, volume 4, pages 1642–1647, 1996. 2 [27] W. B. Thompson and T. chuen Pong. Detecting moving objects. International Journal of Computer Vision, 4:39–57, 1990. 2 [28] T. Veit, F. Cao, and P. Bouthemy. An a contrario decision framework for motion detection. In Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, 2004. 1 [29] M. Watanabe, N. Takeda, and K. Onoguchi. A moving object recognition method by optical flow analysis. In Proc. IEEE Int’l Conf. Pattern Recognition, pages 528–533, 1996. 2

[30] Y. Zhang, S. J. Kiselewich, and W. A. Bauson. A vehicle recognition approach for forward collision warning. Technical report, Delphi Eletronics & Safety, 2004. 2 [31] Z. Zhang. Parameter estimation techniques: a tutorial with application to conic fitting. Proc. IEEE Intell. Veh. Symposium, 15:59–76, 1997. 4

Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) 0-7695-2646-2/06 $20.00 © 2006 IEEE