Regional feature learning using attribute structural analysis in bipartite attention framework for vehicle re-identification

Vehicle re-identification identifies target vehicles using images obtained by numerous non-overlapping real-time surveillance cameras. The effectiveness of re-identification is further challenging because of illumination changes, pose differences of captured images, and resolution. Fine-grained appearance changes in vehicles are recognized in addition to the coarse-grained characteristics like color of the vehicle along with model, and other custom features like logo stickers, annual service signs, and hangings to overcome these challenges. To prove the efficiency of our proposed bipartite attention framework, a novel dataset called Attributes27 which has 27 labelled attributes for each class are created. Our framework contains three major sections: The first section where the overall and semantic characteristics of every individual vehicle image are extracted by a double branch convolutional neural network (CNN) layer. Secondly, to identify the region of interests (ROIs) each branch has a self-attention block linked to it. Lastly to extract the regional features from the obtained ROIs, a partition-alignment block is deployed. The results of our proposed system’s evaluation on the Attributes27 and VeRi-776 datasets has highlighted significant regional attributes of each vehicle and improved the accuracy. Attributes27 and VeRi-776 datasets exhibits 98.5% and 84.3% accuracy respectively which are comparatively higher than the existing methods with 78.6% accuracy

Dual Embedding Expansion for Vehicle Re-identification

Vehicle re-identification plays a crucial role in the management of transportation infrastructure and traffic flow. However, this is a challenging task due to the large view-point variations in appearance, environmental and instance-related factors. Modern systems deploy CNNs to produce unique representations from the images of each vehicle instance. Most work focuses on leveraging new losses and network architectures to improve the descriptiveness of these representations. In contrast, our work concentrates on re-ranking and embedding expansion techniques. We propose an efficient approach for combining the outputs of multiple models at various scales while exploiting tracklet and neighbor information, called dual embedding expansion (DEx). Additionally, a comparative study of several common image retrieval techniques is presented in the context of vehicle re-ID. Our system yields competitive performance in the 2020 NVIDIA AI City Challenge with promising results. We demonstrate that DEx when combined with other re-ranking techniques, can produce an even larger gain without any additional attribute labels or manual supervision