Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding

With the rapid advancement of social economies, intelligent transportation systems are gaining increasing attention. Central to these systems is the detection of abnormal vehicle behavior, which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions. Current research on detecting abnormal traffic behaviors is still nascent, with significant room for improvement in recognition accuracy. To address this, this research has developed a new model for recognizing abnormal traffic behaviors. This model employs the R3D network as its core architecture, incorporating a dense block to facilitate feature reuse. This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure. Additionally, this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions, optimizing the relevance of features for the task at hand. For temporal analysis, a Bi-LSTM layer is utilized to extract and learn from time-based data nuances. This research conducted a series of comparative experiments using the UCF-Crime dataset, achieving a notable accuracy of 89.30% on our test set. Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.