Probability voting and SVM-based vehicle detection in complex background airborne traffic video

Abstract

This paper introduces a novel vehicle detection method combined with probability voting based hypothesis generation (HG) and SVM based hypothesis verification (HV) specialized for the complex background airborne traffic video. In HG stage, a statistic based road area extraction method is applied and the lane marks are eliminated. Remained areas are clustered, and then the canny algorithm is performed to detect edges in clustered areas. A voting strategy is designed to detect rectangle objects in the scene. In HV stage, every possible vehicle area is rotated to align the vehicle along the vertical direction, and the vertical and horizontal gradients of them are calculated. SVM is adopted to classify vehicle and non-vehicle. The proposed method has been applied to several traffic scenes, and the experiment results show it’s effective and veracious for the vehicle detection.