Multipart Vehicle Detection Using Symmetry-Derived Analysis and Active Learning

On-road vehicle detection is a critical operation in automotive active safety systems such as collision avoidance, merge assist, lane change assistance, etc. In this paper, we present VeDAS-Vehicle Detection using Active learning and Symmetry. VeDAS is a multipart-based vehicle detection algorithm that employs Haar-like features and Adaboost classifiers for the detection of fully and partially visible rear views of vehicles. In order to train the classifiers, a modified active learning framework is proposed that selects positive and negative samples of multiple parts in an automated manner. Furthermore, the detected parts from the classifiers are associated by using a novel iterative window search algorithm and a symmetry-based regression model to extract fully visible vehicles. The proposed method is evaluated on seven different datasets that capture varying road, traffic, and weather conditions. Detailed evaluations show that the proposed method gives high true positive rates of over 95% and performs better than existing state-of-the-art rear-view-based vehicle detection methods. Additionally, VeDAS also detects partially visible rear views of vehicles using the residues left behind after detecting the fully visible vehicles. VeDAS is able to detect partial rear views with a detection rate of 87% on a new partially visible rear-view vehicle dataset that we release as part of this paper.