Interactive Offline Tracking for Color Objects ICCV 2007 Yichen Wei , Jian Sun, Xiaoou Tang, Heung-Yeung Shum Microsoft Research Asia Yuan-Hao Lai

[Interactive Offline Tracking for Color Objects] Interactive offline for generic objects User refine tracking result train a object detector from user input use temporal coherence to generate trajectories optimal path found by dynamic programming

[Trial-and-error way] Tracks the object from the first frame User goes back several frames to correct May fail frequently due to continuous changes No guarantee to connect the tracks between two neighboring keyframes consistently and smoothly

works like an optimized “interpolation” [Offline tracking] global optimization problem to find an optimal path in the entire video works like an optimized “interpolation” refine at any frame, restart the optimization 60-100 fps on a 320×240 video

[Minimize global cost] xi: object at i L: user input d(·):data term s(·): smoothness term

[Offline tracking] User starts the tracking by specifying first and last keyframe, system minimizes (1) User then adds or modifies keyframes to correct Exhaustive search: O(WH(wh+B)) →20 seconds Proposed method Detector to quickly reject 98% of non-object regions Trajectory growing algorithm

[Color bin as feature] Spatial configuration of object could easily fail Interactive system should use flexible feature

[Boosted color bins] Adaboosting

[Training] Perturb the position of specified rectangle Scale the object colors Generate about 1000 positive and 3000 negative samples per keyframe for training Using at most 10 color bins are good enough to achieve reasonable training errors

[Experimental Results]

Fast but memory consuming [Pre-computation ] Fast but memory consuming for a 320 × 240 frame, storing all 13 histograms with 8 bins costs about 30MB only integral histograms for several keyframes are loaded Memory cost is 0.7MB/I-frame per frame

[Timing]

[Detection evaluation]

[Conclusion / Future work] Interactive offline tracking for color objects via an efficient global optimization framework Obtain high quality results for difficult videos in a short time Combining other features? Interactive multiple object tracking?

Thank You.