Abstract
The method for synthesizing Gait Energy Image (GEI) is to use the silhouettes overlay of a pedestrian gait cycle, whereas, it is difficult to obtain the silhouettes of a pedestrian gait cycle in practical applications, due to many factors such as pedestrian occlusion and pedestrian direction change, etc. In addition, the existing gait cycle detection methods suffer from limitations. Therefore, a Nonstandard Periodic Gait Energy Image (NP-GEI) is proposed in this paper, which is synthesized without gait cycle detection. And it is verified that the CNN trained by NP-GEI can achieve the same recognition accuracy as that by CP-GEI in most cases. Moreover, we also verify that the CNN, trained by various NP-GEIs which is synthesized by different frame-number silhouettes, can recognize the GEI with robustness in single-view and multi-view scenarios. Finally, a data augmentation method is developed based on NP-GEI, and an experiment is provided to verify the effectiveness of this proposed method.
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Wang, K., Liu, L., Lee, Y., Ding, X., Lin, J. (2019). Nonstandard Periodic Gait Energy Image for Gait Recognition and Data Augmentation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_17
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DOI: https://doi.org/10.1007/978-3-030-31723-2_17
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