The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Activity Classification Based on Gait Energy Image and Coevolutionary Genetic Programming
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification via Minimum Incremental Coding Length
SIAM Journal on Imaging Sciences
Video-Based Human Movement Analysis and Its Application to Surveillance Systems
IEEE Transactions on Multimedia
Human action recognition employing negative space features
Journal of Visual Communication and Image Representation
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In this paper, we present a novel human action recognition approach based on gait energy image (GEI) and minimum incremental coding length (MICL) classifier. GEIs are extracted from video clips and transformed into vectors as input features, and MICL is employed to classify each GEI. We also use multiple cameras to capture GEIs of different views, and the voting strategy is applied after the MICL classification results to improve the overall system performance. Experimental results show that the proposed approach can achieve approximately 95% of accuracy. For practical usage, we also speed up the classification time so that it can be accomplished in a very short time. Moreover, other classification methods are used to classify GEIs and the experimental result shows that MICL is the most suitable classifier for this approach. Besides our recorded action clips, the Weizmann dataset is also used to verify the capability of our approach. The experimental results show that our approach is competitive to other state-of-the-art action recognition methods.