IndGIC: Supervised Action Recognition under Low Illumination

TL;DR

This paper introduces IndGIC, a deep multi-input network with independent gamma correction for low-light action recognition, demonstrating high accuracy on the ARID dataset by effectively enhancing dark videos.

Contribution

It proposes a novel independent gamma intensity correction method combined with a deep multi-input network for improved low-light action recognition.

Findings

Achieves high accuracy on ARID dataset

Effective enhancement of poor-illumination videos

Outperforms existing methods in low-light conditions

Abstract

Technologies of human action recognition in the dark are gaining more and more attention as huge demand in surveillance, motion control and human-computer interaction. However, because of limitation in image enhancement method and low-lighting video datasets, e.g. labeling cost, existing methods meet some problems. Some video-based approached are effect and efficient in specific datasets but cannot generalize to most cases while others methods using multiple sensors rely heavily to prior knowledge to deal with noisy nature from video stream. In this paper, we proposes action recognition method using deep multi-input network. Furthermore, we proposed a Independent Gamma Intensity Corretion (Ind-GIC) to enhance poor-illumination video, generating one gamma for one frame to increase enhancement performance. To prove our method is effective, there is some evaluation and comparison between our method and existing methods. Experimental results show that our model achieves high accuracy in on ARID dataset.