Monocular Vehicle Detection and Tracking
نویسنده
چکیده
This project implements a vehicle detection and tracking system. This framework uses Haar cascade classifier for vehicle detection, and uses car-light features for validation. In order to smooth the detection and refine the detection result further, Kalman tracking is applied to every car hypothesis, additionally, a three-stage tracking is employed. Experiment shows that the car-light validation can largely reduce the false alarms, meanwhile preserving the detection rate; and that tracking can improve the detection result further. Given limited training set, although the Haar cascade classifier produces a large amount of false alarms, the detection system still yields satisfactory result with the refinement and tracking step.
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