Mixed learning based multi-head attention convolutional network for abnormal event detection in video surveillance systems

Abstract

Video Surveillance is generally utilized in highways, residential zones, schools, and other public areas to monitor events happening in those areas, where detecting abnormal events in video surveillance effectively contributes to guaranteeing the safety of public areas. Although various methods have been created in this field, many unsolved issues remain, such as higher computational complexity, irrelevant features, and low learning capability, are exist in the existing methods, which limit them from obtaining an accurate abnormal event detection. Hence, a Supervised Incremental Learning based Multihead Attention Convolutional Network (SIL-MACoN) model is proposed in this research to detect the abnormal events accurately by eliminating the existing drawbacks. The unification of the Multihead Attention (MA) mechanism helps to increase the ability of the SIL-MACoN model to understand complex features by capturing the variances among the features by multiple heads. Moreover, the utilization of incremental and supervised contrastive learning mechanisms improves the MACoN model's learning capability and performance through updating its knowledge without forgetting the previously learned features and producing similar and dissimilar set features for training, respectively. The SIL-MACoN model attains 97.34% accuracy, 97.36% specificity, and 97.33% sensitivity with 90% of training data using the ShanghaiTech Campus Dataset, respectively.