Localizing volumetric motion for action recognition in realistic videos

ABSTRACT

This paper presents a novel motion localization approach for recognizing actions and events in real videos. Examples include StandUp and Kiss in Hollywood movies. The challenge can be attributed to the large visual and motion variations imposed by realistic action poses. Previous works mainly focus on learning from descriptors of cuboids around space time interest points (STIP) to characterize actions. The size, shape and space-time position of cuboids are fixed without considering the underlying motion dynamics. This often results in large set of fragmentized cuboids which fail to capture long-term dynamic properties of realistic actions. This paper proposes the detection of spatio-temporal motion volumes (namely Volume of Interest, VOI) of scale and position adaptive to localize actions. First, motions are described as bags of point trajectories by tracking keypoints along the time dimension. VOIs are then adaptively extracted by clustering trajectory on the motion mainfold. The resulting VOIs, of varying scales and centering at arbitrary positions depending on motion dynamics, are eventually described by SIFT and 3D gradient features for action recognition. Comparing with fixed-size cuboids, VOI allows comprehensive modeling of long-term motion and shows better capability in capturing contextual information associated with motion dynamics. Experiments on a realistic Hollywood movie dataset show that the proposed approach can achieve 20\% relative improvement compared to the state-of-the-art STIP based algorithm.

REFERENCES

• 1: J. Sun, X. Wu, SC. Yan, LF. Cheong, TS. Chua and J. Li. Hierarchical spatio-temporal context modeling for action recognition. CVPR, 2009.
• 2: F. Wang, Y. Jiang, and C. Ngo. Video event detection using motion relativity and visual relatedness. ACM Multimedia, 2008.
• 3: J. Liu, J. Luo, et al. Recognizing realistic actions from videos in the Wild. CVPR, 2009.
• 4: B. Morris, et al. A survey of vision-based trajectory learning and analysis for surveillance. TCSVT, 2008.
• 5: I. Laptev, M. Marsza lek, C. Schmid, et al. Learning realistic human actions from movies. CVPR, 2008.
• 6: D. Batra, T. Chen and R. Sukthankar. Space-Time shapelets for action recognition. IEEE WMVC, 2008.
• 7: L. Gorelick, M. Blank, E. Shechtman, et al. Actions as space-time shapes. TPAMI, 2007.
• 8: R. Tron, et al. A benchmark for the comparison of 3D motion segmentation algorithms. CVPR, 2008.
• 9: Y. Cheng, et al. Mean shift, mode seeking, and clustering. TPAMI, 1995.
• 10: P. Dollar, V. Rabaud, et al. Behavior recognition via sparse spatio-temporal features. In VS-PETS, 2005.
• 11: X. Wu, Y. Zhang, Y. Wu, J. Guo and J. Li. Invariant visual patterns for video copy detection. ICPR, 2008.
• 12: OpenCV: sourceforge.net/projects/opencvlibrary.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Video analysis

General Terms:
Algorithms, Experimentation, Performance

Keywords:
human action recognition, keypoint trajectory, mean-shift clustering, motion subspace learning, realistic videos