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In this paper, we proposed a method to detect and track vehicles efficiently under the AVM camera environment, which was characterized by a wide viewing angle and serious lens distortion. The algorithm was designed for the FVSA(Forward Vehicle Start Alarm) function, a newly required ADAS function. We created independent nearby and distant detectors to compensate for the massive appearance variation of a vehicle because of distance between the target vehicle and the camera. The proposed detectors were designed with Haar-Like Adaboost and tested against independent nearby and distant data. To reduce false positive among the candidates caught by the detector, a pre-generated mask was used in filtering. Next, the candidate vehicle was verified with nearby and distant classifiers. The classifier was a tested HOG(Histogram of Oriented Gradients) feature of SVM(Support Vector Machine). Tracking is then used to extract a particular car within a video, and if nearby and distant candidates were extracted at the same time, a correct candidate would be created by a Selection and Merge process through a calculated IOU value. To evaluate the performance of the proposed algorithm, we selected 20 test videos from 130 videos of actual road conditions, and extracted 1,640 frames.

A Study on the Vehicle Detection and Tracking Using Forward Wide Angle Camera