Accurate and Reliable Detection of Traffic Lights Using Multiclass Learning and Multiobject Tracking
Zhilu Chen, Xinming Huang · IEEE Intelligent Transportation Systems Magazine · 2016
Automatic detection of traffic lights has great importance to road safety. This paper presents a novel approach that combines computer vision and machine learning techniques for accurate detection and classification of different types of traffic lights, including green and red lights both in circular and arrow forms. Initially, color extraction and blob detection are employed to locate the candidates. Subsequently, a pretrained PCA network is used as a multiclass classifier to obtain frame-by-frame results. Furthermore, an online multiobject tracking technique is applied to overcome occasional misses and a forecasting method is used to filter out false positives. Several additional optimization techniques are employed to improve the detector performance and handle the traffic light transitions. When evaluated using the test video sequences, the proposed system can successfully detect the traffic lights on the scene with high accuracy and stable results. Considering hardware acceleration, the proposed technique is ready to be integrated into advanced driver assistance systems or self-driving vehicles. We build our own data set of traffic lights from recorded driving videos, including circular lights and arrow lights in different directions. Our experimental data set is available at http://computing.wpi.edu/Dataset.html.