Abstract
The approach is taken further by Schindler and Van Gool [24], who investigated the detection of actions from very short sequences called snippets. Two separate pathways for motion and shape are considered. Motion is modeled by means of optical flow, computed for different directions and scales. Shape is represented by Gabor filter responses. MAX-pooling and comparison with a set of templates (learned using PCA) yield high-level feature vectors, which are classified through SVMs. In our approach, we feed our classification algorithm by such a powerful feature descriptor, independently computed for each pair of frames.
Highlights
In our paper Novel Kernel-based Recognizers of Human Actions [1], several sentences should be corrected as indicated below
The last three sentences of the second paragraph should read as follows: Jhuang et al [20] present a hierarchical supervised method with spatio-temporal, gradient and flow filters organized in various layers of complexity
The last paragraph should read as follows: The approach is taken further by Schindler and Van Gool [24], who investigated the detection of actions from very short sequences called snippets
Summary
In our paper Novel Kernel-based Recognizers of Human Actions [1], several sentences should be corrected as indicated below. The last three sentences of the second paragraph should read as follows: Jhuang et al [20] present a hierarchical supervised method with spatio-temporal, gradient and flow filters organized in various layers of complexity. In the last layer a multi-class SVM recognizes the human action. The last paragraph should read as follows: The approach is taken further by Schindler and Van Gool [24], who investigated the detection of actions from very short sequences called snippets.
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