Action Recognition in the Dark via Deep Representation Learning

Abstract

Human action recognition for automated video surveillance applications is an interesting but a daunting task especially if the videos are captured in unfavourable lighting conditions. These situations encourage the use of multi-sensor video streams. However, simultaneous activity recognition from multiple video streams is a difficult problem due to their complementary and noisy nature. This paper proposes simultaneous action recognition from multiple video streams using deep multi-view representation learning. Furthermore, it introduces a spatio-temporal feature based correlation filter, for simultaneous detection and recognition of multiple human actions in low-light conditions. We evaluated the performance of our proposed filter with extensive experimentation on nighttime action datasets. Experimental results indicate the effectiveness of deep fusion scheme for robust action recognition in extremely low-light conditions.