A Learning-Based Prediction Model for Baby Accidents

• Main Authors: Wang, Peng-Jie, Lien, Shao-Fu, Lee, Ming-Sui
• Format: Conference Proceeding
• Language: English
• Subjects: Accidents; Computational modeling; Feature extraction; Injuries; Pediatrics; Predictive models; Surveillance
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2019.8803820

According to the statistics in the United Kingdom, more than two million babies and toddlers experienced accidents every year. Despite the places where accidents happened, most of the accidents could've been predicted and prevented. In order to avoid causing injuries by accident, a temporal-pyramid long short-term memory (TP-LSTM) network along with the temporal attention mechanism is proposed to predict whether an accident will happen in the future or not. The proposed network is capable of capturing important information of the video at different temporal resolution and selecting crucial frames that contribute to the accident most. Moreover, the proposed early exponential loss (EEL) function is incorporated to achieve better prediction. The baby video dataset (BVD) containing 670 videos is collected from several video-sharing websites. 320 of which are with accidents and the others are without accidents. The experimental results show that the proposed network attains average precision of 61.13% and the accidents are foreseen 4.196 seconds before the occurrence with 80% recall.