Action recognition from extremely low-resolution thermal image sequence
Action recognition from extremely low-resolution thermal image sequence
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DOI：
10.1109/avss.2017.8078497
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
T. Kawashima;Yasutomo Kawanishi;I. Ide;H. Murase;Daisuke Deguchi;Tomoyoshi Aizawa;M. Kawade
中科院分区：
文献类型：
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作者：
T. Kawashima;Yasutomo Kawanishi;I. Ide;H. Murase;Daisuke Deguchi;Tomoyoshi Aizawa;M. Kawade
针对极低分辨率的热图像序列，提出了一种基于深度学习的动作识别方法。该方法识别人类的日常动作(如行走、坐着、站起等)。和不正常的动作(例如摔倒)，而不考虑隐私。虽然隐私问题可以忽略，但很难计算特征点，并从极低分辨率的热像中获得人体的清晰边缘。针对这些问题，本文提出了一种基于深度学习的动作识别方法，该方法结合卷积层和LSTM层来学习时空表示，其输入是人体区域重心裁剪的热像及其帧差值。通过实验验证了该方法的有效性。
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.