Nighttime vehicle detection and tracking base on spatiotemporal analysis using RCCC sensor

With the consciousness of driving safety growing increasingly, traffic and transportation industry have been devoted to developing Advanced Driver Assistance System (ADAS) recently to help drivers observing the variation of the environmental conditions around the vehicle. Monocular camera is the essential sensor in ADAS to capture the information around the vehicle. Therefore, vision based technology is the main methods to detect obstacles. ADB systems typically exploit a camera that detect the front road view, if the system detect preceding vehicles or oncoming vehicles, the distribution of beam will changed to avoid glare other driver's eyes. Consequently, vehicle detection at nighttime condition is the critical part of an ADB system. This study implements a nighttime vehicle detection method exploiting a RCCC sensor to replace Bayer sensor and aim to obtain a better quality of images at night. Candidate region selection is based on spatiotemporal analysis and three global verifications are adopted to reduce the false detecting results. A new adaptive tracking by-detection framework based on structured output prediction is applied with the detection process.