Abstract
Gait recognition has received significant attention in the recent years due to its applications in numerous fields of computer vision, particularly in automated person identification in visual surveillance and monitoring systems. In this paper, we propose a novel algorithm for gait recognition using spatio-temporal motion characteristics of a person. The proposed algorithm consists of four steps. First, motion features are extracted from video sequence which are used to generate a codebook in the second step. In a third step, the local descriptors are encoded using Fisher vector encoding. Finally, the encoded features are classified using linear Support Vector Machine (SVM). The performance of the proposed algorithm is evaluated and compared with state-of-the-art on two widely used gait databases TUM GAID and CASIA-A. The recognition results demonstrate the effectiveness of the proposed algorithm.
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