Superpixel Tracking. The detail of our motion model: The motion (or dynamical) model of our tracker is assumed to be Gaussian distributed:

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1 Superpixel Tracking Shu Wang 1, Huchuan Lu 1, Fan Yang 1 abnd Ming-Hsuan Yang 2 1 School of Information and Communication Engineering, University of Technology, China 2 Electrical Engineering and Computer Science, University of California at Merced, United States This document includes the illustrations of the training and tracking procedures. Some experimental results are also presented in this document in high resolution. In addition the detail of the Gaussian motion model is presented here. Our algorithm is implemented in MATLAB and is run on a machine of 2.2GHz duo core CPU with 2GB memories. It takes less than 5 seconds per frame during tracking, and 2 seconds or more during every updating procedure. The most timeconsuming part is due to the use of the mean shift clustering algorithm. All the code and data sets will be made available at The detail of our motion model: The motion (or dynamical) model of our tracker is assumed to be Gaussian distributed: p(x t X t 1 ) = N(X t ;X t 1,Ψ) (1) where Ψ is a diagonal covariance matrix whose elements are the standard deviations for location and scale, i.e., σ c and σ s. The values of σ c and σ s dictate how the proposed algorithm accounts for motion and scale change. Furthermore, σ c and σ s are related with the target size and resolution of a certain video. According to these factors and Gaussian probability-density function, we calculate the motion estimatep(x (l) t X t 1 ) of N target candidates {X (l) t } N l=1 as follows: p(x (l) t X t 1 ) = (2πσ 2 i ) 1 2 exp[ 1 2 ( Xi,(l) t X i t 1 2 σ i λ r ) ] i=c,s, l = 1,...,N (2) Parameterλ r represents the resolution of a certain video, and it is obtained by: λ r = v h +v w 2 where parameterv h and v w denotes the height and the width of the frame image, respectively. (3) 1

Figure 1 represents a part of the experimental results

trackers ( tracker, Visual Tracking Decomposition ()

bird2#7 bird2 #11 bird2 #19 lemming#867 lemming#155

#124 woman#32 woman #65 woman #236 Our Tracker Tracker

Four common challenges encountered in tracking.

methods are represented by yellow, red, white, green,

Existing state-of-the-art trackers are not able to

2 Figure 1 represents a part of the experimental results comparison of our tracker and other state-of-the-art trackers ( tracker, Visual Tracking Decomposition () tracker, tracker, tracker, PDAT and PROST tracker). bird2#7 bird2 #11 bird2 #19 lemming#867 lemming#155 lemming #112 transformer#17 transformer#52 transformer #124 woman#32 woman #65 woman #236 Our Tracker Tracker PDAT Tracker Tracker PROST Tracker Figure 1. Four common challenges encountered in tracking. The results by our tracker,,, PROST, Track and PDAT methods are represented by yellow, red, white, green, cyan, and magenta rectangles. Existing state-of-the-art trackers are not able to effectively handle heavy occlusion, large variation of pose and scale, and non-rigid deformation, while our tracker gives more robust results.

Figure 2 illustrates the whole tracking process, and Figure 3 explains for Equation 11 in our paper. (a) (b) (c) (d) (e) (f) (g) (h) (i) Figure 2.

(b) neighborhood of the target location in the last frame, i.e., at state X t 1. (c) segmentation result of (b).

The superpixels colored with red indicates strong likelihood of belonging to the target and those colored with dark blue indicate strong likelihood of

Confidence map. Four target candidate regions corresponding to states X (i) t, i = 1,...,4 are shown both in warped image and the confidence map.

Based on corresponding equation (Equation 1 in our paper), Candidates X (1) t and X (2) t have similar positive confidence C 1 and C 2.

3 Figure 2 illustrates the whole tracking process, and Figure 3 explains for Equation 11 in our paper. (a) (b) (c) (d) (e) (f) (g) (h) (i) Figure 2. Illustration of the use of confidence map for state prediction in tracking process. (a) a new frame at time t. (b) neighborhood of the target location in the last frame, i.e., at state X t 1. (c) segmentation result of (b). (d) the computed confidence map of superpixel. The superpixels colored with red indicates strong likelihood of belonging to the target and those colored with dark blue indicate strong likelihood of belonging to background. (e), (f) and (g), (h) show two target candidates with high and low confidence, respectively. warped image confidence map Figure 3. Confidence map. Four target candidate regions corresponding to states X (i) t, i = 1,...,4 are shown both in warped image and the confidence map. These candidates confidence regionsm i,i = 1,...,4 have the same canonical size (upper right) after normalization. Based on corresponding equation (Equation 1 in our paper), Candidates X (1) t and X (2) t have similar positive confidence C 1 and C 2. Candidates X (3) t and X (4) t have similar negative confidence C 3 and C 4. However, candidate X (2) t covers less target area than X (1) t, and X (4) t covers more background area than X (3) t. Intuitively, target-background confidence of X (1) t should be higher than X (2) t, while confidence ofx (4) t should be lower than X (3) t. These two factors are considered in computing confidence map in the tracking stage.

Figure 4 illustrates the entire training process, and Figure 5

of mean shift tracker and adaptive color-based particle filter.

girl#745 bolt #26 liquor #1236 basketball #169 singer1 #77

The results by the MS tracker, PF method and our algorithm are

It is evident that our tracker is able to handle cluttered

4 Figure 4 illustrates the entire training process, and Figure 5 shows the tracking results with comparisons to color-based trackers of mean shift tracker and adaptive color-based particle filter. Figure 4. Illustration of the training process. girl#745 bolt #26 liquor #1236 basketball #169 singer1 #77 woman#273 Our Tracker Mean Shift Tracker Adaptive Color-based Particle Filter Figure 5. Tracking results with comparisons to color-based trackers. The results by the MS tracker, PF method and our algorithm are represented by red ellipse, green ellipse and yellow rectangles. It is evident that our tracker is able to handle cluttered background (girl and basketball sequences), drastic movement (bolt sequence), heavy occlusion (liquor and woman sequences) and lighting condition change (singer1 sequence).

5 Figure 6 and Figure 7 represents the experimental results comparison of our tracker and other state-of-the-art trackers ( tracker, Visual Tracking Decomposition () tracker, tracker, tracker and PROST tracker). liquor#51 liquor #734 liquor #778 liquor#1187 liquor #1413 liquor #1722 singer1#28 singer1 #126 singer1 #281 basketball #35 basketball #485 basketball #725 Our Tracker Tracker Tracker Tracker PROST Tracker Figure 6. Tracking results with comparisons to other state-of-the-art trackers. The results by our tracker,,, PROST, Track and Track methods are represented by yellow, red, white, green, blue and cyan rectangles.

6 bird1 #33 bird1 #1 bird1 #185 bird1 #268 bird1 #314 bird1 #371 girl#117 girl #1395 girl #15 bolt#2 bolt #184 bolt #35 Our Tracker Tracker Tracker Tracker PROST Tracker Figure 7. Tracking results with comparisons to other state-of-the-art trackers. The results by our tracker,,, PROST, Track and Track methods are represented by yellow, red, white, green, blue and cyan rectangles.

Figure 8 shows the tracking results of figure/ground

The 1st row are original images from six different frames, and

regions, which is obtained by using the appearance model.

We obtain the segmentation results (the 4th row in Figure 8) by

The 5th row are the final tracking results of each frame.

that the target appearance is well learned by our appearance

A simple adaptive threshold-based segmenting on the confidence

finest figure/ground segmentation result (the 4th row).

experiences a full occlusion by a similar target.

that our appearance model gives high measurement to the

Further, when a heavy occlusion occurs, the target disappears

Frame 1287 shows another example of successful tracking under

Frame 1453 shows that our tracker is robust to target pose

pose variance (the 4th and the 5th row).

factors in sequence liguor and our appearance model gives

7 Figure 8 shows the tracking results of figure/ground segmentation across frames on sequence liquor. The 1st row are original images from six different frames, and the 2nd row are the local regions of target based on previous tracking results. The 3rd row are the confidence maps of correspondent local regions, which is obtained by using the appearance model. The 4th row are the figure/ground segmentation results. We obtain the segmentation results (the 4th row in Figure 8) by adopting a simple adaptive threshold-based segmentation method. The 5th row are the final tracking results of each frame. liquor #278 liquor #768 liquor #775 liquor #1287 liquor #1453 liquor #1736 Figure 8. The tracking results of figure/ground segmentation across frames on sequence liquor. The 1st row are original images from six different frames, and the 2nd row are the local regions of target based on previous tracking results. The 3rd row are the confidence maps of correspondent local regions, which is obtained by using the appearance model. The 4th row are the figure/ground segmentation results. The 5th row are the final tracking results of each frame. Frame 278 shows that the target appearance is well learned by our appearance model. A simple adaptive threshold-based segmenting on the confidence map (the 3rd row) of target local region (the 2nd row) gives finest figure/ground segmentation result (the 4th row). Frames 768 and 778 show a full procedure when the target experiences a full occlusion by a similar target. It can be clearly seen from the confidence map (the 3rd row) that our appearance model gives high measurement to the superpixels that belong to target. Further, when a heavy occlusion occurs, the target disappears from our confidence map. Frame 1287 shows another example of successful tracking under full occlusion. Frame 1453 shows that our tracker is robust to target pose changes that it still gives fine tracking result under severe pose variance (the 4th and the 5th row). Frame 1736 shows that our tracker survives all challenging factors in sequence liguor and our appearance model gives accurate segmentation result by the end of this sequence.

8 The plots from the paper in greater resolution are also reproduced in Figure lemming basketball PROST bolt bird transformer liquor woman PROST bird girl singer Figure 9. Quantitative evaluations of our tracker and other state-of-the-art trackers. Trackers of ours,, Visual Tracking Decomposition (), Track, Track, PROST are presented by color magenta, red, black, blue, cyan and green.

THE recent years have witnessed significant advances

THE recent years have witnessed significant advances IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 4, APRIL 2014 1639 Robust Superpixel Tracking Fan Yang, Student Member, IEEE, Huchuan Lu, Senior Member, IEEE, and Ming-Hsuan Yang, Senior Member, IEEE