Describing Trajectory of Surface Patch for Human Action Recognition on RGB and Depth Videos

By Yan Song, Siyi Liu, Jinhui Tang. 2015

30 citations recorded by the source corpus.

This letter proposes a new feature describing the trajectories of surface patches (ToSP) on human bodies for action recognition by a novel scheme of utilizing RGB and depth videos. RGB data contains appearance information by which we track specific patches on body surfaces while depth data contains spatial information by which we describe surface patches. Specifically, we use spatial-temporal interest points as initial points to track in two directions. A ToSP is extracted by keeping the neighborhood in point cloud of each point on the trajectory. By using the temporal pyramid, ToSPs are matched on several levels based on the surface feature extracted from ToSP segments. The proposed feature captures both the shape and position variations of surface patches, thus it has the advantages of trajectories and local spatial-temporal features. The experiment results show that the proposed feature outperforms the existing trajectories based features and depth features.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, background): “Multimodality analysis of RGB+D action videos is studied by [39, 34, 59, 20, 22, 87, 88, 31, 63, 23, 55, 34].”
RGB-D-based action recognition datasets: A survey (2016, neutral): “ross view) [47] 1. 84.93 2. 91.79(0◦), 86.67(−25◦), 88.89(+25◦), 75.56(−50◦), 77.78(+50◦) 1. D 2. D 1. CS (Half training, half test) 2. 0◦ training NJUST [49] 1. ToSP+SVM [49] 2. BSC+Spatial-Temporal [100] 1. 98.4 2. 94.7 1. C,D 2. D 1. LOSubO 2. LOSubO 27 DMLSmart Actions [43] 1. SVM-NNSC + Propose”