Published on Mon Mar 11 2019

A Hybrid Framework for Action Recognition in Low-Quality Video Sequences

Tej Singh, Dinesh Kumar Vishwakarma

Vision-based activity recognition is essential for security, monitoring and surveillance applications. Real-time analysis having low-quality video contains less information about surrounding due to poor illumination. Therefore, it needs a more robust and integrated model for low quality and night security operations.

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Abstract

Vision-based activity recognition is essential for security, monitoring and surveillance applications. Further, real-time analysis having low-quality video and contain less information about surrounding due to poor illumination, and occlusions. Therefore, it needs a more robust and integrated model for low quality and night security operations. In this context, we proposed a hybrid model for illumination invariant human activity recognition based on sub-image histogram equalization enhancement and k-key pose human silhouettes. This feature vector gives good average recognition accuracy on three low exposure video sequences subset of original actions video datasets. Finally, the performance of the proposed approach is tested over three manually downgraded low qualities Weizmann action, KTH, and Ballet Movement dataset. This model outperformed on low exposure videos over existing technique and achieved comparable classification accuracy to similar state-of-the-art methods.