Gait-Based Action Recognition via Accelerated Minimum Incremental Coding Length Classifier

摘要

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.