Spatio-temporal fall event detection in complex scenes using attention guided LSTM

• Published in: Pattern recognition letters 2020-02, Vol.130, p.242-249
• Main Authors: Feng, Qi, Gao, Chenqiang, Wang, Lan, Zhao, Yue, Song, Tiecheng, Li, Qiang
• Format: Article
• Language: English
• Subjects: Datasets; Fall event dataset; Fall event detection; Feature extraction; LSTM; Public safety
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2018.08.031

•A new fall event dataset in crowded and complex scenes is created.•A novel fall event detection architecture based on attention guided LSTM is proposed.•The experimental results show that the proposed method outperforms the state-of-the-art methods. Fall events are one of the greatest risks for public safety, especially in some complex scenes with large number of people. Nevertheless, there are few researches on fall detection in complex scenes, and even no public datasets. A fall event dataset in crowded and complex scenes is constructed. Aiming at detecting fall events in complex scenes, we further propose an attention guided LSTM model. Our method provides the spatial and temporal locations of fall events, which are indispensable information for danger alarm in complex public scenes. Specifically, the effective YOLO v3 is employed to detect pedestrian in videos, and followed by a tracking module. CNN features are extracted for each tracked bounding boxes. Fall events are detected by the attention guided LSTM. Experimental results show that our method achieves good performance, outperforming the state-of-the-art methods.