A discriminative deep transfer metric learning method called DDTML is proposed for cross-scenario person re-identification (Re-ID). To develop the Re-ID model in a new scenario, a large number of pairwise cross-camera-view person images are deemed necessary. However, this work is very expensive due to both monetary cost and labeling time. In order to solve this problem, a DDTML for cross-scenario Re-ID is proposed using the transferring data in other scenarios to help build a Re-ID model in a new scenario. Specifically, to measure distribution difference across scenarios, a maximum mean discrepancy based on class distribution called MMDCD is proposed by embedding the discriminative information of data into the concept of the maximum mean discrepancy. Unlike most metric learning methods, which usually learn a linear distance to project data into the feature space, DDTML uses a deep neural network to develop the multilayers nonlinear transformations for learning the nonlinear distance metric, while DDTML transfers discriminative information from the source domain to the target domain. By bedding the MMDCD criteria, DDTML minimizes the distribution divergence between the source domain and the target domain. Experimental results on widely used Re-ID datasets show the effectiveness of the proposed classifiers.
At present, the level of urbanization in China has exceeded 50% and the number of car ownership has reached 140 million. The consequent problem of traffic congestion has become increasingly prominent. It is increasingly important that how to get the basic vehicle information in real time and accurately so that the traffic department can timely manage the vehicles of the specific road sections and intersections. At present, some related methods and algorithms have high real-time performance, but the accuracy is not high or the contrary. Accordingly, this paper proposes a method of automatic vehicle detection based on YOLOV2 framework which has both real-time and accuracy. The method improves the YOLOv2 framework model, optimizes the important parameters in the model, expands the grid size, and improves the number and sizes of anchors in the model, which can automatically learn the vehicle features and realize real-time and high-precision vehicle automatic detection and vehicle class identification. The evaluation on home-made dataset shows that compared with YOLOv2 and Faster RCNN, the accuracy rate is raised to 91.80 %, the recall rate to 63.86 %.
A novel discriminative deep transfer learning method called DDTML is proposed for Cross-scenario Person Reidentification( Re-ID). Using a deep neural network, DDTML learns a set of hierarchical nonlinear transformations for Cross-scenario Person Re-identification by transferring discriminative knowledge from the source domain to the target domain. Meanwhile, taking account of the inherent characteristics of Re-ID data sets, in order to reduce the distribution divergence between the source data and the target data, DDTML minimizes a new maximum mean discrepancy based on Class Distribution called MMDCD at the top layer of the network. Experimental results on widely used Re-identification datasets show the effectiveness of the proposed classifiers.