A Scale and Rotation Invariant Approach to Tracking Human Body Part Regions in Videos Yihang BoHao Jiang Institute of Automation, CAS Boston College.

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Presentation transcript:

A Scale and Rotation Invariant Approach to Tracking Human Body Part Regions in Videos Yihang BoHao Jiang Institute of Automation, CAS Boston College

Challenges

Previous Rectangular Part Methods Templates with Different scales Templates with Different rotations If the target scale and rotation are unknown, local part extraction becomes a very slow process.

Solution: Finding Body Part Regions

Overview of the Method  We track human body part regions (arm, leg and torso) in videos.  Our model considers spatial and temporal coupling among parts.  It is invariant to scale and rotation.

Tracking Body Part Regions

The Non-tree Model Body part coupling between two successive video frames

Part Region Candidates Object class independent Region Proposals Object class independent Region Proposals Superpixels Ian Endres, and Derek Hoiem, “Category Independent Object Proposals”, ECCV P.F. Felzenszwalb and D.P. Huttenlocher, Efficient Graph-Based Image Segmentation International Journal of Computer Vision, Volume 59, Number 2, September 2004.

3D Superpixels Video segmentation (3D superpixels) usually do not directly give human part regions.

Partial Background Removal (Optional) warping ……

Criteria Shape Matching Part Distance Part Overlap Relative Ratio Shape Changes Position Changes Appearance Changes

Distance Term

Overlap Region Overlap Region Overlap

Size Ratio Part Size Ratio

Shape Consistency Across Frames Shape Consistency

Motion Smoothness Motion Continuity

Color Consistency Appearance Consistency

Inference on a Loopy Graph … We assign region candidates to each of the body part node so that the objective function is minimized.

Convert to a Chain … … Linear meta-graph

Convert to a Chain … … Unfortunately, there are too many whole body configurations in each video frame.

Convert to a Chain … … Solution: we find the best-N whole body configurations in each video frame.

Cycle Removal

Cycle Breaking

Find Best-N Body Configurations on a Cycle Best-N (with torso1) Best-N (with torso2) + Best-N (with torso1,2) Best-N (with torso3) + Best-N (with torso1,2,3) … Best-N (with torso M) + Best-N (with torso1..M)

Region Tracking on a Trellis Frame 1Frame 2Frame k Best-N Body configurations

Sample Results on Five Test Videos V1 V2 V3 V4 V5

Comparison Result [N-best] D. Park, D. Ramanan. "N-Best Maximal Decoders for Part Models”, ICCV 2011.

Quantitative results Comparison Result

Conclusion By tracking body part regions, we can achieve efficient scale and rotation invariant human pose tracking. This method can be used for human tracking in complex sports videos.

Thank You