Abstract
Vehicle reidentification refers to the mission of matching vehicles across nonoverlapping cameras, which is one of the critical problems of the intelligent transportation system. Due to the resemblance of the appearance of the vehicles on road, traditional methods could not perform well on vehicles with high similarity. In this paper, we utilize hypergraph representation to integrate image features and tackle the issue of vehicles re-ID via hypergraph learning algorithms. A feature descriptor can only extract features from a single aspect. To merge multiple feature descriptors, an efficient and appropriate representation is particularly necessary, and a hypergraph is naturally suitable for modeling high-order relationships. In addition, the spatiotemporal correlation of traffic status between cameras is the constraint beyond the image, which can greatly improve the re-ID accuracy of different vehicles with similar appearances. The method proposed in this paper uses hypergraph optimization to learn about the similarity between the query image and images in the library. By using the pair and higher-order relationship between query objects and image library, the similarity measurement method is improved compared to direct matching. The experiments conducted on the image library constructed in this paper demonstrates the effectiveness of using multifeature hypergraph fusion and the spatiotemporal correlation model to address issues in vehicle reidentification.
Highlights
Traffic video surveillance plays a vital role in ensuring public safety
The main task of vehicle reidentification is to search for images of the same vehicle captured by multicameras in various areas on the precondition that the target vehicle is known in the surveillance videos
Based on the theory of deep learning, this paper proposes a cross-camera vehicle tracking method based on feature matching and hypergraph fusion
Summary
Traffic video surveillance plays a vital role in ensuring public safety. A critical part of traffic video surveillance in the urban and major cities is the monitoring of vehicles, which include detection, tracking, and classification. The query image is matched with image sets and, combined with spatiotemporal information of the vehicle image, achieves multitarget identification from multichannel traffic surveillance videos. (1) We use different algorithms to extract different features of vehicles and construct hypergraphs, respectively, to represent their relationships. In the form of hypergraphs, we can express their deeper relationships (2) We explored hypergraph learned algorithms to integrate multiple traditional feature extraction methods. This contribution combines the advantages of multiple methods to improve the accuracy of apparency matching (3) The trickiest challenge of vehicle re-ID problem is the high similarity of the vehicles. We applied the spatiotemporal features of the vehicle as additional constraints to improve the matching accuracy of similar vehicles
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
