基于高置信局部特征的车辆重识别优化算法

In solving vehicle re-identification problems, different vehicle regions have different recognition degree of confidence. Based on this observation, we propose a vehicle re-identification optimization algorithm that takes advantage of the high-confidence local features. First, the vehicle key point detection algorithm is utilized to obtain the corresponding multiple key points' coordinate information of the vehicles, and to divide the vehicle brand extension regions and other prominent local regions. As the brand extension region is the most salient region, we propose to improve the degree of confidence of the local region in the testing phase. We also utilize a multi-layer convolutional neural network for processing the input images, cutting the convolutional features into several parts based on the obtained local regions, and acquiring feature tensors representing global and key regional information. Then, a fully connected layer is applied to combine the above features and output a one-dimensional vector for loss function calculating. In the testing phase, to reduce the target distances of vehicles with the same local identification, we propose to utilize the global features together with the high-confidence local features obtained by trained brand extension region extraction branch. Experiments on the widely used vehicle re-identification VehicleID dataset show that the proposed algorithm is effective.