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This paper proposes a vision based multiple vehicle detection and tracking system. Vehicle tail light information is used to localize vehicle potential region, then each candidate is verified by a back propagation neural network (BPNN) trained by Gabor feature set. In the multiple vehicle tracking stage, multiple scale vehicle tracking, same color vehicle occlusion and observation model updating problem...
In this study, an real-time multiple-vehicle detection and tracking system in complex environments with automatic lane detection and reducing shadow effects is proposed. First, lane marks can be automatically detected, and this automation makes the proposed system more possible to deploy in the practical traffic conditions. Second, Histogram Extension (HE) addresses how to remove the effects of weather...
In this paper, we address the problem of vehicle detection and tracking with low-angle cameras by combining windshield detection and feature points clustering, effectively fusing several primitive image features such as color, edge and interest point. By exploring various heterogenous features and multiple vehicle models, we achieve at least two improvements over the existing methods: higher detection...
This paper presents methods for vision-based detection and tracking of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. The goal of this research is to develop suitable methods for automatic visual traffic surveillance to perform detection, tracking and traffic parameter estimation of multiple vehicles in real time as well as tackle environment illumination...
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