Video-Based Fall Detection Using Human Pose and Constrained Generative Adversarial Network

Falls are a major health threat for older people. A timely assistance can reduce the extent of physical injury caused by the falls. Currently, low-cost and convenient video surveillance systems based on ordinary RGB cameras are widely used for improving the safety of people. The fall detection is a research hotspot in intelligent video surveillance. In this work, we propose an unsupervised fall detection method. The proposed method first converts the RGB video frames into human pose images to eliminate the background interferences and focus on human motion and protect privacy. Afterwards, the future pose images are predicted by using the continuous historical human pose images based on a constrained generative adversarial network (GAN). Finally, the prediction errors of the human pose images and the anomaly scores of actual poses calculated by using the traditional hand-crafted features are used to realize the fall detection. As compared to the existing vision-based fall detection methods, the proposed method possesses strong generalization ability, and is robust to environmental interferences and small local occlusions, and effectively protects the privacy, and avoids time-consuming data annotations. In addition, in this work, a new large-scale and comprehensive fall dataset is created and is available for download. We perform extensive experiments on the public benchmark datasets and the proposed dataset. The results demonstrate the validity and superiority of the proposed method.