Semantic information based vehicle relative orientation and taillight detection

Vehicle taillight detection is an important topic in the fields of collision avoidance systems and autonomous vehicles. By analyzing the changes in the taillights of vehicles, the intention of the driver can be understood, which can prevent possible accidents. This paper presents a convolutional neural network architecture capable of segmenting taillight pixels by detecting vehicles and uses already computed features to segment taillights. The network is composed of a Faster RCNN that detects vehicles and classify them based their orientation relative to the camera and a subnetwork that is responsible for segmenting taillight pixels from vehicles that have their rear facing the camera. Multiple Faster RCNN configurations were trained and evaluated. This work also presents a way of adapting the ERFNet semantic segmentation architecture for the purpose of taillight extraction, object detection and classification. The networks were trained and evaluated using the KITTI object detection dataset.