Abstract
Temporal shape variations intuitively appear to provide a good cue for human activity modeling. In this paper, we lay out a novel framework for human action recognition based on fuzzy log-polar histograms and temporal self-similarities. At first, a set of reliable keypoints are extracted from a video clip (i.e., action snippet). The local descriptors characterizing the temporal shape variations of action are then obtained by using the temporal self-similarities defined on the fuzzy log-polar histograms. Finally, the SVM classifier is trained on these features to realize the action recognition model. The proposed method is validated on two popular and publicly available action datasets. The results obtained are quite encouraging and show that an accuracy comparable or superior to that of the state-of-the-art is achievable. Furthermore, the method runs in real time and thus can offer timing guarantees to real-time applications.
Highlights
Human action recognition has received and still receives considerable attention in the field of computer vision due to its vital importance to many video content analysis applications [1]
The nonrigid nature of human body and clothes in video sequences resulting from drastic illumination changes, changing in pose, and erratic motion patterns presents the grand challenge to human detection and action recognition [2]
While the real-time performance is a major concern in computer vision, especially for embedded computer vision systems, the majority of state-of-the-art action recognition systems often employ sophisticated feature extraction and/or learning techniques, creating a barrier to the real-time performance of these systems
Summary
Human action recognition has received and still receives considerable attention in the field of computer vision due to its vital importance to many video content analysis applications [1]. While the real-time performance is a major concern in computer vision, especially for embedded computer vision systems, the majority of state-of-the-art action recognition systems often employ sophisticated feature extraction and/or learning techniques, creating a barrier to the real-time performance of these systems. This suggests that there is an inherent trade-off between recognition accuracy and computational overhead
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