An algorithm to estimate mean vehicle speed from MPEG Skycam video
Multimedia Tools and Applications
Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Vehicle logo recognition using a SIFT-based enhanced matching scheme
IEEE Transactions on Intelligent Transportation Systems
Vehicle model recognition from frontal view image measurements
Computer Standards & Interfaces
Vehicle classification from traffic surveillance videos at a finer granularity
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Using adaptive background subtraction into a multi-level model for traffic surveillance
Integrated Computer-Aided Engineering
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This paper presents a method of automated virtual loop assignment and direction-based motion estimation. The unique features of our approach are that: 1) a number of loops are automatically assigned to each lane. The merit of doing this is that it accommodates pan-tilt-zoom actions without needing further human interaction; 2) the size of the virtual loops is much smaller for estimation accuracy; and 3) the number of virtual loops per lane is large. The motion content of each block may be weighted and the collective result offers a more reliable and robust approach in motion estimation. Comparing this with traditional inductive loop detectors, there are a number of advantages. Our simulation results indicate that the proposed method is effective in type classification