Visual perception enhancement fall detection algorithm based on vision transformer

Fall detection is a crucial research topic in public healthcare. With advances in intelligent surveillance and deep learning, vision-based fall detection has gained significant attention. While numerous deep learning algorithms prevail in video fall detection due to excellent feature processing capabilities, they all exhibit limitations in handling long-term spatiotemporal dependencies. Recently, Vision Transformer has shown considerable potential in integrating global information and understanding long-term spatiotemporal dependencies, thus providing novel solutions. In view of this, we propose a visual perception enhancement fall detection algorithm based on Vision Transformer. We utilize Vision Transformer-Base as the baseline model for analyzing global motion information in videos. On this basis, to address the model's difficulty in capturing subtle motion changes across video frames, we design an inter-frame motion information enhancement module. Concurrently, we propose a locality perception enhancement self-attention mechanism to overcome the model's weak focus on local key features within the frame. Experimental results show that our method achieves notable performance on the Le2i and UR datasets, surpassing several advanced methods.