Simultaneous detection and tracking using deep learning and integrated channel feature for ambint traffic light recognition

Abstract

Perceiving the information about ambient traffic lights is an inevitable task for autonomous vehicles. To deal with the issue, this work develops an accurate and fast traffic light recognition strategy for autonomous vehicles by an onboard camera. In this paper, deep learning based detection and object tracking is synthesized to determine the position and color of traffic lights. First, the mechanism of simultaneous detection and tracking is founded, wherein the video reading module, convolutional neural network (CNN) module, integrated channel feature tracking (ICFT) module are run simultaneously. Then, the respective modules of detection and tracking are introduced. CNN model is designed and trained to obtain the position of traffic lights utilized as initial information for tracking. ICFT is applied to continually track the traffic light targets and determine the light color. Finally, the effectiveness of the presented method is validated via comparing with the state of art. Experiments results indicate that the proposed technique can improve the accuracy and speed of recognition. Our contributions are: (1) Establish a mechanism for simultaneous detection and tracking of traffic lights; (2) Carefully design the CNN architecture and ICFT features; (3)The precision and recall rates on traffic lights recognition reached 0.962 and 0.909, respectively, and the recognition speed reached 21.4FPS (GPU: Nvidia Titan Xp).