Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2005 Papers
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Extracting Moving People from Internet Videos
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Dynamic color flow: a motion-adaptive color model for object segmentation in video
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
POSECUT: simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In general, human behavior analysis relies on a sequence of human segments, e.g. gait recognition aims to address human identification based on people's manners of walking, and thus relies on the segmented silhouettes. Background subtraction is the most widely used approach to segment foreground, while dynamic scenes make it difficult to work. In this paper, we propose to combine Mean-Shift-based tracking with adaptive scale and Graphcuts-based segmentation with label propagation. The average precision on a number of sequences is 0.82, and the average recall is 0.72. Besides, our method only requires weak user interaction and is computationally efficient. We compare our method with its variant without label propagation, as well as GrabCut. For the tracking module only, we compare Mean Shift with several state-of-the-art methods (i.e. OnlineBoost, SemiBoost, MILTrack, FragTrack).