基于运动和区域感知对抗学习的热成像坠落检测（CS CV）---用户7095611

自动坠落检测技术是保证人体健康和安全的关键技术。 基于家庭的摄像系统用于探测坠落，经常使人们的隐私处于危险之中。 热成像摄像头可以部分 / 完全模糊面部特征，从而保护人的隐私。 另一个挑战是，与日常生活活动相比，跌倒的发生率较低。 由于下降很少发生，它是非平凡的学习算法，由于类不平衡。 为了解决这些问题，我们使用热成像摄像机将跌落探测作为对抗性框架内的异常检测。 我们提出了一种新型的对抗网络，它由两个通道3d 卷积自动编码器组成，每个通道处理视频序列和光流，然后重建热数据和光流输入序列。 介绍了微分约束、感兴趣区域跟踪技术和计算重建误差的联合鉴别器。较大的重建错误表明在视频序列中发生了跌倒。 在一个公开的热落差数据集上的实验显示了与标准基线相比所获得的优越的结果。

原文题目：Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging

原文：Automatic fall detection is a vital technology for ensuring health and safety of people. Home based camera systems for fall detection often put people's privacy at risk. Thermal cameras can partially/fully obfuscate facial features, thus preserving the privacy of a person. Another challenge is the less occurrence of falls in comparison to normal activities of daily living. As fall occurs rarely, it is non-trivial to learn algorithms due to class imbalance. To handle these problems, we formulate fall detection as an anomaly detection within an adversarial framework using thermal imaging camera. We present a novel adversarial network that comprise of two channel 3D convolutional auto encoders; one each handling video sequences and optical flow, which then reconstruct the thermal data and the optical flow input sequences. We introduce a differential constraint, a technique to track the region of interest and a joint discriminator to compute the reconstruction error. Larger reconstruction error indicates the occurrence of fall in a video sequence. The experiments on a publicly available thermal fall dataset show the superior results obtained in comparison to standard baseline.

原文作者：Vineet Mehta

原文地址：https://arxiv.org/abs/2004.08352