Attention driven vehicle re-identification and unsupervised anomaly detection for traffic understanding

Vehicle re-identification and anomaly detection are useful tools in traffic analytics applications. Vehicle reidentification is particularly challenging due to variations in viewpoint, illumination and occlusion. Moreover, the reality of multiple vehicles having the same make and model hinders the design of traditional deep network-based solutions. In this work, we leverage an attention-based model which learns to focus on different parts of a vehicle by conditioning the feature maps on visible key-points. We use triplet embedding to reduce the dimensionality of the features obtained from the ensemble of networks trained using different datasets. To address the problem of anomaly detection, we design an unsupervised algorithm to detect and localize anomalies in traffic scenes. To handle moving cameras, we use the results obtained from tracking to generate anomaly proposals which are then filtered in successive steps. We show the effectiveness of our method on the Nvidia AI City vehicle re-identification dataset, where we obtain mean Average Precision (mAP) score of 60.78% placing us at the 8th position out of 84 participating teams. In addition, we achieved the S3 score of 22.07% for vehicle anomaly detection.