Hybrid LED traffic light detection using high-speed camera

Automatic traffic light detection (TLD) plays an important role for driver-assistance system and autonomous vehicles. State-of-the-art TLD systems showed remarkable results by exploring visual information from static frames. However, traffic lights from different countries, regions, and manufactures are always visually distinct. The existing large intra-class variance makes the pre-trained detectors perform good on one dataset but fail on the others with different origins. One the other hand, LED traffic lights are widely used because of better energy efficiency. Based on the observation LED traffic light flashes in proportion to the input AC power frequency, we propose a hybrid TLD approach which combines the temporally frequency analysis and visual information using high-speed camera. Exploiting temporal information is shown to be very effective in the experiments. It is considered to be more robust than visual information-only methods.

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

[2]  Ralf G. Herrtwich,et al.  Making Bertha See , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[3]  Mohan M. Trivedi,et al.  Traffic Light Detection at Night: Comparison of a Learning-Based Detector and Three Model-Based Detectors , 2015, ISVC.

[4]  Wen Gao,et al.  Vicept: link visual features to concepts for large-scale image understanding , 2010, ACM Multimedia.

[5]  Mohan M. Trivedi,et al.  Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives , 2016, IEEE Transactions on Intelligent Transportation Systems.

[6]  Zheng Liu,et al.  Saliency Map Generation by the Convolutional Neural Network for Real-Time Traffic Light Detection Using Template Matching , 2015, IEEE Transactions on Computational Imaging.

[7]  Margaret Martonosi,et al.  Leveraging Smartphone Cameras for Collaborative Road Advisories , 2012, IEEE Transactions on Mobile Computing.

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

[9]  Nanning Zheng,et al.  A multi-feature fusion based traffic light recognition algorithm for intelligent vehicles , 2014, Proceedings of the 33rd Chinese Control Conference.

[10]  Ingmar Posner,et al.  Exploiting 3D semantic scene priors for online traffic light interpretation , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[11]  Pietro Cerri,et al.  Robust real-time traffic light detection and distance estimation using a single camera , 2015, Expert Syst. Appl..

[12]  F. Lindner,et al.  Robust recognition of traffic signals , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[13]  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.

[14]  Toshiaki Fujii,et al.  Tracking an LED array transmitter for visible light communications in the driving situation , 2010, 2010 7th International Symposium on Wireless Communication Systems.

[15]  Johann Marius Zöllner,et al.  Visual state estimation of traffic lights using hidden Markov models , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[16]  Toshiaki Fujii,et al.  High-speed-camera image processing based LED traffic light detection for road-to-vehicle visible light communication , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[17]  Myoungho Sunwoo,et al.  Multiple exposure images based traffic light recognition , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[18]  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.

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

[20]  J. Canny.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Mohan M. Trivedi,et al.  Traffic Light Detection: A Learning Algorithm and Evaluations on Challenging Dataset , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[22]  Toshiaki Kondo,et al.  Red traffic light detection using fast radial symmetry transform , 2014, 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

[23]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[25]  Fernando Santos Osório,et al.  Traffic lights detection and state estimation using Hidden Markov Models , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[26]  Shoji Kawahito,et al.  Image-sensor-based visible light communication for automotive applications , 2014, IEEE Communications Magazine.

[27]  Slobodan Ilic,et al.  Semantic Segmentation Based Traffic Light Detection at Day and at Night , 2015, GCPR.