Action recognition from extremely low-resolution thermal image sequence
Takayuki Kawashima, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Daisuke Deguchi, Tomoyoshi Aizawa, Masato Kawade · 2017
This paper proposes a Deep Learning-based action recognition method from an extremely low-resolution thermal image sequence. The method recognizes daily actions by humans (e.g. walking, sitting down, standing up, etc.) and abnormal actions (e.g. falling down) without privacy concerns. While privacy concerns can be ignored, it is difficult to compute feature points and to obtain a clear edge of the human body from an extremely low-resolution thermal image. To address these problems, this paper proposes a Deep Learning-based action recognition method that combines convolution layers and an LSTM layer for learning spatio-temporal representation, whose inputs are the thermal images and their frame differences cropped by the gravity center of human regions. The effectiveness of the proposed method was confirmed through experiments.