Action recognition in low quality videos by jointly using shape, motion and texture features

Saimunur Rahman, John See, Chiung Ching Ho

Abstract: Shape, motion and texture features have recently gained much popularity in their use for human action recognition. While many of these descriptors have been shown to work well against challenging variations such as appearance, pose and illumination, the problem of low video quality is relatively unexplored. In this paper, we propose a new idea of jointly employing these three features within a standard bag-of-features framework to recognize actions in low quality videos. The performance of these features were extensively evaluated and analyzed under three spatial downsampling and three temporal downsampling modes. Experiments conducted on the KTH and Weizmann datasets with several combination of features and settings showed the importance of all three features (HOG, HOF, LBP-TOP), and how low quality videos can benefit from the robustness of textural features.