Traffic light recognition in varying illumination using deep learning and saliency map

The accurate detection and recognition of traffic lights is important for autonomous vehicle navigation and advanced driver aid systems. In this paper, we present a traffic light recognition algorithm for varying illumination conditions using computer vision and machine learning. More specifically, a convolutional neural network is used to extract and detect features from visual camera images. To improve the recognition accuracy, an on-board GPS sensor is employed to identify the region-of-interest, in the visual image, that contains the traffic light. In addition, a saliency map containing the traffic light location is generated using the normal illumination recognition to assist the recognition under low illumination conditions. The proposed algorithm was evaluated on our data sets acquired in a variety of real world environments and compared with the performance of a baseline traffic signal recognition algorithm. The experimental results demonstrate the high recognition accuracy of the proposed algorithm in varied illumination conditions.

[1]  Chuan Huang,et al.  Traffic light detection during day and night conditions by a camera , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[2]  Chris Urmson,et al.  Traffic light mapping and detection , 2011, 2011 IEEE International Conference on Robotics and Automation.

[3]  Fawzi Nashashibi,et al.  Real time visual traffic lights recognition based on Spot Light Detection and adaptive traffic lights templates , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[4]  Tao Jin,et al.  Robust and Real-Time Traffic Lights Recognition in Complex Urban Environments , 2011, Int. J. Comput. Intell. Syst..

[5]  Evangelos Dermatas,et al.  Traffic Lights Detection in Adverse Conditions using Color, Symmetry and Spatiotemporal Information , 2012, VISAPP.

[6]  Joo In-Hak,et al.  Detection of traffic lights for vision-based car navigation system , 2006 .

[7]  M. Omachi,et al.  Traffic light detection with color and edge information , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[8]  Gang Tao,et al.  The recognition and tracking of traffic lights based on color segmentation and CAMSHIFT for intelligent vehicles , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[9]  Sebastian Thrun,et al.  Traffic light mapping, localization, and state detection for autonomous vehicles , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  Fawzi Nashashibi,et al.  Traffic light recognition using image processing compared to learning processes , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Javier J. Sánchez Medina,et al.  Suspended traffic lights detection and distance estimation using color features , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.