InfAR dataset: Infrared action recognition at different times

Action recognition (AR) is one of the most important tasks in video analysis and computer vision. Recently a large number of related methods have been proposed. While most of these methods are investigated on AR datasets collected from the visible spectrum, the AR problem under infrared scenarios still has not attracted much attention. There is even few public infrared datasets available for supporting the fundamental evaluation requirements of this research. To this issue, this work aims to emphasize the importance of the infrared AR problem in applications and arouse researchers' attention on this task. Specifically, we construct a new Infrared Action Recognition (InfAR) dataset captured at different times, including in summer and winter, and explore how discriminable actions in our InfAR dataset are with the state-of-the-art pipelines based on low-level features and deep convolutional neural network (CNN), respectively. Our results reveal: (1) In all, dense trajectory feature can achieve the best performance while the appearance features, e.g., HOG, have relatively poorer performance; (2) the encoding method of vector of locally aggregated descriptors is evidently better than that of the widely-used Fisher Vector; (3) the late fusion facilitates a better performance than early fusion; (4) action videos captured in winter is more discriminable than in summer; (5) compared to appearance information, the motion information is more essential for infrared action recognition and utilizing this information through deep CNN can improve greatly the performance. The best performance achieved on our dataset is 76.66% (Average Precision), leaving a reasonable space for further exploring the insights underlying such type of infrared AR problem and accordingly designing proper techniques to further promote the performance on this specifically constructed InfAR dataset.