Traffic light detection and recognition in intersections based on intelligent vehicle

To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.

Traffic signal detection and classification in street views using an attention model

Detecting small objects is a challenging task.We focus on a special case:the detection and classification of traffic signals in street views.We present a novel framework that utilizes a visual attention model to make detection more efficient,without loss of accuracy,and which generalizes.The attention model is designed to generate a small set of candidate regions at a suitable scale so that small targets can be better located and classified.In order to evaluate our method in the context of traffic signal detection,we have built a traffic light benchmark with over 15,000 traffic light instances,based on Tencent street view panoramas.We have tested our method both on the dataset we have built and the Tsinghua–Tencent 100K(TT100K)traffic sign benchmark.Experiments show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both datasets.It is competitive with state-of-theart specialist traffic sign detectors on TT100K,but is an order of magnitude faster.To show generality,we tested it on the LISA dataset without tuning,and obtained an average precision in excess of 90%.

Traffic light detection and recognition for autonomous vehicles

Traffic light detection and recognition is essential for autonomous driving in urban environments. A camera based algorithm for real-time robust traffic light detection and recognition was proposed, and especially designed for autonomous vehicles. Although the current reliable traffic light recognition algorithms operate well under way, most of them are mainly designed for detection at a fixed position and the effect on autonomous vehicles under real-world conditions is still limited. Some methods achieve high accuracy on autonomous vehicle, but they can＇t work normally without the aid of high-precision priori map. The authors presented a camera-based algorithm for the problem. The image processing flow can be divided into three steps, including pre-processing, detection and recognition. Firstly, red-green-blue （RGB） color space is converted to hue-saturation-value （HSV） as main content of pre-processing. In detection step, the transcendental color threshold method is used for initial filterings, meanwhile, the prior knowledge is performed to scan the scene in order to quickly establish candidate regions. For recognition, this article use histogram of oriented gradients （HOG） features and support vector machine （SVM） as well to recognize the state of traffic light. The proposed system on our autonomous vehicle was evaluated. With voting schemes, the proposed can provide a sufficient accuracy for autonomous vehicles in urban enviroment.