Turn Signal Detection During Nighttime by CNN Detector and Perceptual Hashing Tracking

Detecting vehicle turn signals at night is critical for both assistant driving systems and autonomous driving systems. In this paper, we propose a novel method that consists of detection and tracking modules to achieve a high level of robustness. For nighttime vehicle detection, a Nakagami-image-based method is used to locate the regions containing vehicle lights. At the same time, a set of vehicle object proposals is generated using a region proposal network based on convolutional neural network (CNN) feature maps. Then, the light regions and proposals are combined to generate the regions of interest (ROIs) for the further detection. Vehicle candidates are extracted from the ROIs using a softmax classifier with CNN-based features. For the tracking module, we propose a perceptional hashing algorithm to track these vehicle candidates. During the tracking, turn signals are detected by analyzing the continuous intensity variation of the vehicle box sequences. Experimental results for typical sequences show that the proposed method can robustly detect and track a vehicle in front with over 95% accuracy and recognize the turning signals in night scenes with a detection rate of over 90%. The vehicle detection method improves the miss rate of state-of-the-art systems by more than 20%. In addition, the proposed vehicle tracking method outperforms other state-of-the-art systems.