No abstract

No abstract

Vehicle re-identification (re-id) is an important issue in the transportation and vehicle tracking area. The existing FastReID framework has made significant improvements in data preprocessing, model structure and parameter configuration of re-id. However, the FastReID did not consider the directional differences between vehicle images, which makes it difficult to balance the image pairs with different directional differences during calculating the similarity between vehicles. To make a further improvement based on the FastReID, this paper proposes an Orientation Estimation and Regional Feature Fusion method, named OERFF. In OERFF, the orientation estimation model is trained to judge the orientation and the main region of a vehicle in an image. Then the dedicated region feature model is utilized to extract the regional feature of a vehicle according to its proper orientation. Finally, a feature fusion strategy is applied with weighted distance according to the orientation difference of vehicles to further improve the identification accuracy. Extensive experiments are conducted based on three workbench datasets and results show that the proposed method can improve the mAP by 2.5% on the VeRi-766 dataset and 3.2% on the VERI-Wild dataset. The accuracy of rank-1 is improved by 2.4% on the VehicleID dataset.

To improve the safety protection capability of automobile while driving, and better to play the role of CST in protecting automobile, aiming at some factors affecting road safety, and combined with the design requirements of control system, the article researched the judge method about driving safety level, human-computer interaction response alerts and emergency measures. And by using cooperative control structure mode on multi-system and the centralized cooperative strategy, the article designed the cooperative control scheme on multisystem containing CST for automobile safety. This cooperative control scheme had better real-time performance and simpler design than a single control system.

No abstract