Evaluating and Extending Trajectory Features for Activity Recognition
5.6 Summary and Future Work
In this chapter, we have presented the co-recognition approach to images and videos that detects, matches, and segments multiple sets of identical objects or actions in
Fig. 5.19 The qualitative results of the proposed algorithm on our dataset containing multiple actions in a video clip. (a) Selected frames from two videos containing temporally distinguished actions. (b) Selected frames from two videos containing spatially distinguished actions
an unsupervised way. The basic idea is to grow initial matches into reliable object correspondences in a multi-layer match-growing framework and analyze their rela-tions by their matching regions or volumes. Unlike other unsupervised segmentation or object discovery methods, it effectively considers both geometry and appearance to discover the detailed dense matches and segmentation from complex images or videos. We have shown its robust performance on a variety of unsupervised vision applications, such as unsupervised object detection and segmentation [8,9], image retrieval, symmetry analysis [6], action detection [51], and 3D reconstruction.
While the proposed approach provides wide applications and impressive results on complex scenes and videos, the current method still has some limitations. As already noted in Sect.5.2, co-recognition can detect an object correspondence un-der the geometric and photometric distinctiveness of the objects appeared in given images or videos. The condition, however, is not strictly satisfied in usual data.
whole-part relations, as well as object interactions. In the future, pursuing the di-rection, we plan to improve the co-recognition approach for more complex scene understanding based on mutual relations of various objects.
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