400. 500. Frame number. 0. 0.2. 0.4. 0.6. 0.8. 1. 1.2. Confidence score. Tracking confidence plot - ClifBar. Tracking co
1
Robust Visual Tracking via Hierarchical Convolutional Features Supplementary Document Chao Ma, Jia-Bin Huang, Xiaokang Yang, and Ming-Hsuan Yang
F
The current object tracking benchmarks, such as the OTB [1], [2] and the VOT [3], [4], do not provide separate validation and test sets. To address the lack of validation set in the OTB [1], [2] and the VOT [3], [4] datasets, we use a separate set of video sequences (that are not in OTB and VOT) introduced by the MEEM method (http://cs-people.bu.edu/jmzhang/MEEM/MEEM.html) as our validation dataset for our hyper-parameter selection. The MEEM dataset contains 10 challenging sequences with featured attributes, such as heavy occlusion (dance, boxing1, boxing2, ped1, and ped2), abrupt illumination changes (carRace, and billieJean), low contrast (ball, ped2, rocky, and billieJean), and significant non-rigid deformation (latin, ball, carRace, dance, and billieJean). Figure 1 shows sample frames from the MEEM dataset, which contains approximately 7500 frames in total. In the following, we describe how we choose the three important threshold values regarding 1) 2) 3)
the re-detection threshold T0 for activating the detection module; the acceptance threshold (= 1.5T0 ) for adopting detection results; and the stability threshold (= T0 ) for conservatively updating the long-term filter.
In evaluating our tracker on the MEEM dataset, we disable our re-detection module of the HCFT* so that we can obtain more samples of failure cases. We refer this baseline implementation by HCFT*-no-det. We use the same long-term filter of the HCFT* to compute the tracking confidence scores of the HCFT*-no-det method. Over the 10 video sequences in the MEEM dataset, we observe that the when tracking failures occur (i.e., the overlap success scores are below 0.5), the confidence scores computed by the long-term filter are generally smaller than 0.2. We present in Figure 2 sample results showing the confidence scores (blue curves) as well as the overlap success scores in terms of Intersection over Union (red curves) on all the MEEM sequences. For the ball, carRace, and ped1 sequences, the HCFT*-no-det method fails to track target objects after a few hundreds of frames due to heavy occlusion or the out-of-the-view movement. We thus empirically set the re-detection threshold T0 to 0.2. For setting the acceptance threshold, we use a larger value than the re-detection threshold T0 to conservatively accept the detection results. Specifically, we use a parameter sweep scheme by setting the re-detection threshold to K ∗ T0 , where K ∈ [1, 2] with an increment of 0.1 (i.e., K = 1, 1.1, · · · , 1.9, 2.0). We find that setting the acceptance threshold to 1.5T0 gives the best averaged performance on the 10 MEEM video sequences. For the stability threshold, we start by setting this threshold to 0.2 as the long-term filter has to be updated with high confidence. We then sweep the stability threshold from 0.2 to 0.5. However, we find that the performance is not sensitive to the stability threshold on all the 10 validation sequences. Figure 3 (left) shows an example on the ball sequence. We thus set the stability threshold to equal T0 as 0.2. In addition, we validate the appropriateness of the stability threshold on the test sequences in the OTB dataset. We have the same observation that tracking confidence scores are consistent when the stability threshold between 0.2 and 0.5, as an example of the test sequence lemming shown in Figure 12 (bottom right) of the manuscript. For convenience, we present Figure 12 (bottom right) of the manuscript in Figure 3 (right) in this response. We first use the MEEM dataset as our validation set to find the three threshold values. Then we fix the hyper-parameters in all our experiments in OTB and VOT sequences. Our results show that the tracker work well on the testing datasets (OTB and VOT). For example, we observe that when tracking failures occur (i.e., the overlap success scores are below 0.5), the confidence scores computed by the long-term filter are generally smaller than 0.2 on most of the OTB sequences. Figure 4 shows the tracking confidence scores with the comparison to tracking success scores on all the 100 OTB sequences.
R EFERENCES [1] [2] [3] [4]
Y. Wu, J. Lim, and M.-H. Yang, “Online object tracking: A benchmark,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2013. 1 ——, “Object tracking benchmark,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 9, pp. 1834–1848, 2015. 1 M. Kristan et al., “The visual object tracking VOT2014 challenge results,” in Proc. of European Conf. on Computer Vision Workshop, 2014. 1 M. Kristan, J. Matas, A. Leonardis, M. Felsberg, L. Cehovin, G. Fern´andez, T. Voj´ır, G. H¨ager, G. Nebehay, and R. P. Pflugfelder, “The visual object tracking VOT2015 challenge results,” in Proc. of IEEE Int. Conf. on Computer Vision Workshop, 2015. 1 [5] J. Zhang, S. Ma, and S. Sclaroff, “MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization,” in Proc. of European Conf. on Computer Vision, 2014.
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Fig. 1: Overview of the MEEM dataset. Red: Tracking results of HCFT*-no-det. Green: Ground-truth bounding boxes.
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Fig. 2: Tracking confidence scores and overlap ratio scores of HCFT*-no-det. Note that when tracking failures occur (i.e., the overlap ratio scores are below 0.5), the confidence scores are generally below 0.2.
Tracking confidence plot - ball
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Fig. 3: Tracking confidence scores on the ball and lemming sequences. We test the stability threshold for updating the long-term filter. Confidence scores are consistent with threshold values between 0.2 and 0.5.
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Fig. 4: Tracking confidence scores and overlap ratio scores of HCFT* on the OTB-2015 dataset. Note that when tracking failures occur (i.e., the overlap ratio scores are below 0.5), the confidence scores are generally below 0.2.
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1
1.2
Confidence score
1400
Frame number Tracking confidence plot - FaceOcc2
1.2
Tracking confidence score Overlap ratio (IoU)
Confidence score
1200
0.6
0.2
0 0
Frame number Tracking confidence plot - FaceOcc1
1.2
0.4
0.2
0
20
0.6
Confidence score
0.8
0.6
Confidence score
0.8
Confidence score
0.8
0
Confidence score
Tracking confidence score Overlap ratio (IoU)
1
0.6
Tracking confidence plot - Dudek
1.2 Tracking confidence score Overlap ratio (IoU)
1
0
Confidence score
Tracking confidence plot - DragonBaby
1.2
Tracking confidence score Overlap ratio (IoU)
1
0.2
Confidence score
Tracking confidence plot - Doll
1.2
Tracking confidence score Overlap ratio (IoU)
1
Confidence score
Confidence score
Tracking confidence score Overlap ratio (IoU)
1
0.6
0.4
0.2
0 0
20
40
60
80
Frame number
100
120
140
0
20
40
60
80
100
Frame number
Fig. 4: (Continue) Tracking confidence scores and overlap ratio scores of HCFT* on the OTB-2015 dataset. Note that when tracking failures occur (i.e., the overlap ratio scores are below 0.5), the confidence scores are generally below 0.2.
5
Tracking confidence plot - Mhyang
1.2
Tracking confidence plot - MotorRolling
1.2
0.8
0.8
0.8
0.4
0.6
0.4
0.4
0.2
0.2
0.2
0
0
0
0
500
1000
0
1500
Frame number Tracking confidence plot - Rubik
20
40
60
80
100
120
140
160
180
0
Frame number Tracking confidence plot - Shaking
1.2
50
100
150
200
250
0.6
0.4
0.2
0 0
Frame number Tracking confidence plot - Singer1
1.2
Tracking confidence score Overlap ratio (IoU)
200
400
600
800
1000
0
Frame number Tracking confidence plot - Singer2
1.2
Tracking confidence score Overlap ratio (IoU)
1
0.8
0.4
0.2
0
500
1000
1500
Frame number Tracking confidence plot - Skater2
1.2
50
100
150
200
250
300
350
400
Tracking confidence score Overlap ratio (IoU)
0.4
50
100
150
200
250
300
350
Tracking confidence score Overlap ratio (IoU)
0.6
0.4
0 0
Frame number Tracking confidence plot - Skating2.1
1.2
50
100
150
200
250
300
350
400
0
Frame number Tracking confidence plot - Skating2.2
1.2
Tracking confidence score Overlap ratio (IoU)
0.8
0.8
0.8
0.6
0.4
0.6
0.4
0.2
0.2
0.2
0.2
0
0
0
0
0
50
100
150
200
250
300
350
400
450
0
Frame number Tracking confidence plot - Soccer
1.2
50
100
150
200
250
300
350
400
0
Frame number Tracking confidence plot - Subway
1.2
Tracking confidence score Overlap ratio (IoU)
100
200
300
400
Confidence score
0.8
Confidence score
0.8
Confidence score
1
Confidence score
1
0.4
500
100
200
300
400
0
500
Frame number Tracking confidence plot - Suv
Tracking confidence score Overlap ratio (IoU)
0.8
0.2
0
0.2
0 0
50
100
150
200
250
300
350
400
20
40
60
80
100
120
140
160
180
Tracking confidence score Overlap ratio (IoU)
0.4
50
100
150
200
250
300
350
400
Tracking confidence score Overlap ratio (IoU)
200
400
600
800
1000
0
Frame number Tracking confidence plot - Trans
1.2
Tracking confidence score Overlap ratio (IoU)
0.8
0.6
0.4
0.2
0.2
0.2
0.2
0
0
0
0
0
50
100
150
200
250
300
350
400
0
Frame number Tracking confidence plot - Twinnings
1.2
50
100
150
200
250
300
350
400
0
Frame number Tracking confidence plot - Vase
1.2
Tracking confidence score Overlap ratio (IoU)
50
100
150
200
250
300
Tracking confidence score Overlap ratio (IoU)
40
60
80
100
120
140
0
0.2
0
0.2
0 0
100
200
300
Frame number
400
500
50
100
150
Frame number
200
250
300
0.6
0.4
0.2
0 0
Confidence score
Confidence score
1
0.8
Confidence score
1
0.8
Confidence score
1
0.8
0.4
50
100
150
200
250
Frame number
300
350
400
200
300
450
400
500
600
0.6
0.4
0.2
0 0
1400
Tracking confidence score Overlap ratio (IoU)
1
0.2
100
Tracking confidence score Overlap ratio (IoU)
0.6
1200
Frame number Tracking confidence plot - Woman
1.2
0.8
0.4
1000
0 20
Frame number Tracking confidence plot - Walking2
1.2
Tracking confidence score Overlap ratio (IoU)
0.6
800
0.4
1
0.4
600
0.6
0.8
0.6
400
0.2
0
Frame number Tracking confidence plot - Walking
1.2
Confidence score
0.8
Confidence score
0.8
Confidence score
0.8
Confidence score
0.8
0.4
80
Tracking confidence score Overlap ratio (IoU)
1
0.4
200
Tracking confidence score Overlap ratio (IoU)
1
0.4
70
Frame number Tracking confidence plot - Trellis
1.2
1
0.6
60
0.4
1
0.6
50
0.6
1
0.6
40
0 0
Frame number Tracking confidence plot - Toy
1.2
30
0.2
0
0
Frame number Tracking confidence plot - Tiger2
1.2
0.6
0.2
0 0
Frame number Tracking confidence plot - Tiger1
1.2
0.4
Confidence score
0.8
Confidence score
0.8
Confidence score
0.8
Confidence score
0.8
0.2
20
Tracking confidence score Overlap ratio (IoU)
1
0.4
10
Tracking confidence score Overlap ratio (IoU)
1
0.4
160
Frame number Tracking confidence plot - Sylvester
1.2
1
0.6
140
0.4
1
0.6
120
0.6
1
0.6
100
0 0
1.2
Tracking confidence score Overlap ratio (IoU)
80
0.2
Frame number Tracking confidence plot - Surfer
1.2
60
Tracking confidence score Overlap ratio (IoU)
1
0.4
40
Frame number Tracking confidence plot - Skiing
Tracking confidence score Overlap ratio (IoU)
1
0.6
20
1.2
1
0.6
2000
0.2
0 0
Frame number Tracking confidence plot - Skating1
1.2
0.6
0.2
0 0
2000
0.4
0.2
0
0
0.6
Confidence score
1
0.8
Confidence score
1
0.8
Confidence score
1
0.6
1500
Tracking confidence score Overlap ratio (IoU)
0.8
0.4
1000
Tracking confidence score Overlap ratio (IoU)
1
0.6
500
Frame number Tracking confidence plot - Skater
1.2
0.8
Confidence score
Confidence score
0.4
0.6
0.2
0.2
Confidence score
0.6
Confidence score
0.8
Confidence score
0.8
Confidence score
1
Confidence score
Confidence score
1
Tracking confidence score Overlap ratio (IoU)
Confidence score
Tracking confidence score Overlap ratio (IoU)
1
0.6
Tracking confidence plot - RedTeam
1.2
Tracking confidence score Overlap ratio (IoU)
1
0
Confidence score
Tracking confidence plot - Panda
1.2 Tracking confidence score Overlap ratio (IoU)
1
1.2
Confidence score
Tracking confidence plot - MountainBike
1.2
Tracking confidence score Overlap ratio (IoU)
Tracking confidence score Overlap ratio (IoU)
0 0
100
200
300
Frame number
400
500
0
100
200
300
400
500
600
Frame number
Fig. 4: (Continue) Tracking confidence scores and overlap ratio scores of HCFT* on the OTB-2015 dataset. Note that when tracking failures occur (i.e., the overlap ratio scores are below 0.5), the confidence scores are generally below 0.2.s
