Robust Nighttime Vehicle Detection by Tracking and Grouping Headlights

• Published in: IEEE transactions on intelligent transportation systems Vol. 16; no. 5; pp. 2838 - 2849
• Main Authors: Zou, Qi, Ling, Haibin, Luo, Siwei, Huang, Yaping, Tian, Mei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cameras; Context; intelligent transportation system; multiple object tracking; Roads; Robustness; Tracking; Vehicle detection; vehicle headlight pairing; Vehicles; vehicle headlight pairing; Vehicle detection; intelligent transportation system; multiple object tracking
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2015.2425229

Nighttime traffic surveillance is difficult due to insufficient and unstable appearance information and strong background interference. We present in this paper a robust nighttime vehicle detection system by detecting, tracking, and grouping headlights. First, we train AdaBoost classifiers for headlights detection to reduce false alarms caused by reflections. Second, to take full advantage of the complementary nature of grouping and tracking, we alternately optimize grouping and tracking. For grouping, motion features produced by tracking are used by headlights pairing. We use a maximal independent set framework for effective pairing, which is more robust than traditional pairing-by-rules methods. For tracking, context information provided by pairing is employed by multiple object tracking. The experiments on challenging datasets and quantitative evaluation show promising performance of our method.