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

TL;DR

This paper introduces a hybrid framework combining image enhancement and silhouette analysis to improve action recognition accuracy in low-quality, poorly lit videos for security applications.

Contribution

It presents a novel illumination-invariant recognition model that outperforms existing techniques on low-exposure video datasets.

Findings

Achieved higher recognition accuracy on low-light videos

Outperformed existing methods on low exposure datasets

Maintained comparable accuracy to state-of-the-art approaches

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.