Bayes Saliency-Based Object Proposal Generator for Nighttime Traffic Images

• Published in: IEEE transactions on intelligent transportation systems Vol. 19; no. 3; pp. 814 - 825
• Main Authors: Kuang, Hulin, Yang, Kai-Fu, Chen, Long, Li, Yong-Jie, Chan, Leanne Lai Hang, Yan, Hong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes rule; Bayesian analysis; Feature extraction; Fuel consumption; Generators; Image edge detection; Intelligent transportation systems; Night; nighttime vehicle detection; Object proposal; Proposals; Salience; saliency detection; State of the art; Support vector machines; Vehicle detection
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2017.2702665

Object proposal is one of the most key pre-processing steps for nighttime vehicle detection systems in intelligent transportation systems. However, most current object proposal methods are developed on daytime data sets, and these methods demonstrate unsatisfactory results when they are used on nighttime images. Therefore, this paper presents a novel Bayes saliency-based object proposal generator for nighttime RGB traffic images to generate a modest and accurate set of proposals, which are more likely to be vehicles for preceding vehicle detection. First, we propose a new Bayes saliency detection approach in which prior estimation, feature extraction, weight estimation, and Bayes rule are used to compute saliency maps. Then, we propose a simple but effective object proposal generator based on the Bayes saliency map. Multi-scale sliding window, proposal rejecting, scoring, and non-maximum suppression are combined to generate a modest and effective set of proposals. Experimental results demonstrate that our proposed approach generates a modest set of proposals and outperforms some state-of-the-art methods on nighttime images in terms of various evaluation metrics. Furthermore, our proposed object proposal approach can improve the detection performance and the speed of several state-of-the-art vehicle detection approaches.