Deformable Blur Sensing and Regression Analysis ReID Feature Fusion for Multitarget Multicamera Tracking Systems in Highway Scenarios

Abstract

In highway scenarios, the rapid motion of vehicles can cause deformation and blur in camera footage, significantly affecting the accuracy of vehicle detection and re-identification (ReID) in multitarget multicamera tracking (MTMCT) systems. To address this issue, this article develops the deformable and blur sensing and regression analysis ReID feature fusion MTMCT system (DSRF). First, a deformable and blur sensing detection module (DFB) in DSRF is designed to overcome the limitations of cameras in capturing fast-moving objects, thereby accurately detecting vehicles moving at high speeds on highways. Then, a regression-based ReID feature fusion algorithm (RARF) in DSRF is proposed, which enhances ReID features by modeling the relationship between vehicle motion and its features, thereby better associating the detected vehicles in consecutive frames into trajectories and establishing intertrajectory relationships. Finally, extensive experiments are conducted on the highway surveillance traffic (HST) dataset developed by our team and the public dataset (CityFlow). Promising results are achieved, validating the effectiveness of our proposed method.