Vision Based Real Time Object Tracking Using ...

International Conference On Systemics, Cybernetics and Informatics

Vision Based Real Time Object Tracking Using Moving Camera Shyam Lal

Mahesh Chandra

Gopal Krishna Upadhyay

Associate Professor Deptt. of E & C Engg., Moradabad Institute of Technology, Moradabad(U.P.),India [email protected]

Associate Professor Deptt. of E & C Engg, Birla Institute of Technology, Mesra, Ranchi (Jharkhand),India [email protected]

Director Landmark Technical Campus Didauli, J .P. Nagar (U.P.) ,India, [email protected]

Abstract This paper presents vision based real time object tracking using a moving camera. In this paper a real time object tracking using joint colour-texture histogram, mean shift and moving camera is presented. The proposed method enjoys the double tracking feature. Tracking of some specific objects in real life is of particular interest due to its enhanced automation in public security surveillance, robotics, and traffic control etc. The novel object tracking method is presented in this paper by using the joint colour texture histogram to represent a target and then applying it to mean shift framework and then use it in moving camera tracking system. Apart from the conventional colour histogram features, the texture features of the object are also extracted by using the local binary pattern (LBP) technique to represent the object. Compared with the traditional static camera based method that use the fix target region for tracking, the proposed method covers a wide area effectively. The simulation & experimental results validate that the proposed method improves greatly the tracking area, accuracy and efficiency. It can robustly track the target under complex scenes, such as similar target and background appearance, on which the traditional schemes may fail to track. Keywords: Mean Shift, LBP Scheme, Joint Colour-Texture Histogram

Mean Shift is an iterative kernel-based deterministic procedure which converges to a local maximum of the measurement function with certain assumptions on the kernel behaviours. Furthermore, mean shift is a low complexity algorithm, which provides a general and reliable solution to object tracking. Currently, a widely used form of target representation is the colour histogram, which could be viewed as the discrete probability density function (PDF) of the target region. Colour histogram is an estimating mode of point sample distribution and is very robust in representing the object appearance. However, using only colour histograms in mean shift tracking has some problems. First, the spatial information of the target is lost. Second, when the target has similar appearance to the background, colour histogram will become invalid to distinguish them. For a better target representation, the gradient or edge features have been used in combination with colour histogram. The texture patterns, which reflect the spatial structure of the object are effective features to represent and recognize targets. Since the texture features introduce new information that the colour histogram does not convey, using the joint colour-texture histogram for target representation is more reliable than using only colour histogram in tracking complex scenes. The idea of combining colour and edge for target representation has been exploited by researchers. However, how to utilize effectively both the colour intensity and texture features is still a difficult problem. The local binary pattern (LBP) technique is very effective to describe the image texture features. LBP has advantages such as fast computation and rotation invariance, which facilitates the wide usage in the fields of texture analysis, image retrieval, face recognition, image segmentation, etc. Robust object tracking using joint colour-texture histogram LBP was successfully applied to the detection of moving objects via background subtraction [8-12]. In LBP, each pixel is assigned a texture value, which can be naturally combined with the colour value of the pixel to represent targets. Nguyen et al. employed the image intensity and the LBP feature to construct a two- dimensional histogram representation of the target for tracking thermographic and monochromatic video [13]. In this paper, we adopt the LBP scheme to represent the target texture feature and then propose a joint colour-texture histogram method for a more distinctive and effective target representation. The major uniform LBP patterns are used to identify the key points in the target region and then form a mask for joint colour-texture feature selection. The proposed target representation scheme eliminates smooth background and reduces noise in the tracking process. Compared with the traditional RGB colour space based target representation, it efficiently exploits the target structural information and hence achieves better tracking performance

1. Introduction Real-time object tracking is a critical task in computer vision applications. In real life object cannot restricted in a predefined region, so we need something more than static camera. Many tracking algorithms have been proposed to overcome the difficulties arising from noise, occlusion, clutter and changes in the foreground object or in the background environment [1-7]. Among the various tracking algorithms, mean shift tracking algorithms have recently become popular due to their simplicity and efficiency. The mean shift algorithm was originally proposed by Fukunaga and Hostetler for data clustering [8]. Copyright © 2012 Paper Identification Number: CS-1.7 This peer-reviewed paper has been published by the Pentagram Research Centre (P) Limited. Responsibility of contents of this paper rests upon the authors and not upon Pentagram Research Centre (P) Limited. Copies can be obtained from the company for a cost.

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Vision Based Real Time Object Tracking Using Moving Camera

with fewer mean shift iterations and higher robustness to

various interferences of background and noise in complex scenes [14-28]. The rest of the paper is organized as follows: Section 2 describes software & hardware frameworks, Section 3 give simulation & experimental results, and finally section 4 concludes the paper.

2. Software and Hardware Framework MATLAB has been used to process the image by colour texture histogram method. Final design is tested with a realtime system setup in the laboratory. The hardware setup is logically divided into 3 subsystems, namely the vision system, PC-based controller and moving target system. The system is controlled by a PC-based controller utilizing inexpensive webcam. Simple environment is prepared for real-time experiments. The PC-based controller used is a Lenovo laptop, Equipped with Pentium 4, 1.70GHz processor and 1GB of RAM. USB port version 2.0 must be available at the PCbased controller to ensure high image transfer speed. The image processing algorithm and crisp based rules is implemented in MATLAB. Webcam is able to capture an image or video at a frame rate of 30fps with banding filter frequency of 60Hz. Images acquired from webcam can be access by the PC-based controller through USB port. The moving target system consists of the moving target and a workspace. System operation flow chart of the motion tracking system is shown in Figure 1,object tracking diagram is shown in Figure 2, the measurement of coordinates of the object is shown in Figure 3 and object tracking flow chart is shown in Figure 4.

Figure 4.Object tracking flow chart

3. Simulation & Experimental Results

Figure1. System Operation Flow Chart Figure 5. Tracked Image A

Figure 2. Diagram of object tracking

Figure 6. Tracked Image B Figure 3. Measurement of coordinates of object

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International Conference On Systemics, Cybernetics and Informatics

Figure 9. Tracked Image E

Figure 10. Tracked Image F

Figure 7. Tracked Image C

Figure 11. Tracked Image G Figure 11. Tracked Image G

Figure 8. Tracked Image D

Figure 12. Tracked Image H

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Vision Based Real Time Object Tracking Using Moving Camera

with fewer mean shift iterations and higher robustness to various interferences of background and noise in complex scenes. Proposed method is very effective in finding of the coordinates of object in terms of Θ and Φ. Future research will be devoted to find the distance of moving object from the camera as well as further improving image segmentation and edge-based feature extraction methods.

[1].

[2].

Figure 13. Tracked Image I [3].

[4].

[5].

[6].

[7].

Figure 14. Tracked Image J [8].

Table 1. Tracking data of different images

[9].

[10].

[11].

4. Conclusion [12].

This paper proposed a new method for vision based real time object tracking using joint colour-texture histogram, mean shift and moving camera. Proposed method adopted the LBP scheme to represent the target texture feature and then a joint colour-texture histogram method for a more distinctive and effective target representation. The major uniform LBP patterns have used to identify the key points in the target region and then form a mask for joint colour-texture feature selection. The proposed target tracking scheme eliminates smooth background and reduces noise in the tracking process. Compared with the traditional RGB colour space based target representation, it efficiently exploits the target structural information and hence achieves better tracking performance

[13].

[14].

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International Conference On Systemics, Cybernetics and Informatics

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Processing from Department of Electronics & Communication Engineering, Birla Institute of Technology, Mesra, Ranchi (Jharkhand)-India. He has been Associate Professor in the department of Electronics & Communication Engineering, Moradabad Institute of Technology, Moradabad (U.P.)-India. He has published more than 33 papers in the area of Digital Image Processing and Wireless Communication & Computing at International/National Journals & Conferences. He has been Guest Editor of Special issue on “Digital Signal & Image Processing Applications” of IJSISE (Inderscience Journal). He is Member of IEEE (Since 2011), life member of Indian Society of Technical Education (ISTE), New Delhi-India (LM-39989), International Association of Engineers (IAENG), Hong Kong (M.No.-103480) and Life member International Association of Computer Science and Information Technology (IACSIT), Singapore (LM- 80333445). He has more than ten years teaching experience. His area of interest includes Digital Image Processing, Digital Signal Processing and Wireless Communication. Mahesh Chandra received B.Sc. from Agra University, Agra (U.P.)-India in 1990 and A.M.I.E. from I.E.I., Kolkata (W.B.)India in winter 1994. He received M.Tech. from J.N.T.U., Hyderabad-India in 2000 and Ph.D. from AMU, Aligarh (U.P.)-India in 2008. He has worked as Reader & HOD in the Department of Electronics & Communication Engineering at S.R.M.S. College of Engineering and Technology, Bareilly (U.P.)-India from Jan 2000 to June 2005. He is presently working as Associate Professor in the Department of Electronics & Communication Engineering, B.I.T., Mesra, Ranchi (Jharkhand)-India. He is a Life Member of ISTE, New Delhi-India and Member of IEI Kolkata (W.B.)-India. He has published more than 34 research papers in the area of Speech, Signal and Image Processing at International/ National level conferences and journals. He is currently guiding 05 Ph.D. students in the area of Speech, Signal and Image Processing. His areas of interest are Speech, Signal and Image Processing. Gopal Krishna Upadhyay received B.Sc. (Maths) from Kanpur University, Kanpur (U.P.)-India in 1992,, M.Sc. (Electronics) from V.B.S. Purvanchal university Jaunpur (U.P.)-India in 1994 and Ph.D.(Solid State Physics) from V.B.S. Purvanchal University Jaunpur (U.P.) in 2001.He worked as Lecturer in the Department of Physics, K.A.P.G.A. Allahabad(U.P.)-India from July 1994 to 2003 and Asstt. Professor in Department of Physics of United College of Engg., Allahabad (U.P.)-India from July 2000 to July 2003. He had also worked as Professor & Dy. Director in T.M.I.M.T, Teerthankar Mahaveer University, Moradabad (U.P.)-India and Director, SSITM Kasganj-Moradabad(U.P.)India and He is presently working as Director, Landmark Technical Campus Didauli, J .P. Nagar(U.P.)–India. He has more than 16 years of teaching experience. He has published more than 32 papers in the National/International level. He has also published 4 books. His area of interest is solid state devices, computing & image processing.

Author’s Biography Shyam Lal received B.Tech. (with Hons.) in Electronics & Communication Engineering from Bundelkhand Institute of Engineering & Technology(B.I.E.T) Jhansi (U.P.)-India and M.Tech.(with Hons.) in Electronics & Communication Engineering from National Institute of Technology, Kurukshetra (Haryana)India in year 2001 & 2007, respectively . He is pursuing Ph.D. degree in the area of Digital Image

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