IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Integrating Representative and Discriminative Models for Object Category Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Evaluating combinational color constancy methods on real-world images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Transit signal priority (TSP), which is one of the most important issues in intelligent transportation systems, aims to provide priority signals with an advanced inspection system to public transport vehicles. In this paper, by introducing the advanced object detection technique into intelligent transport systems, we propose an automatic bus detection algorithm and apply it to the transit signal priority (TSP) system. The contributions of this paper fall into two folds: (1) we propose a bus detection algorithm. In this algorithm, an illumination-independent color feature is used for bus detection, which is useful in practical illumination environments. In addition, the widely-used sparse representation technique is extended to cost-sensitive kernel sparse representation, that can effectively combine different features for bus detection. (2) A transit signal priority control scheme is proposed based on the bus detection results. This control scheme optimizes the traffic lights signal according to whether a bus is coming or not. Experimental and simulation results show that the proposed intelligent TSP system based on bus detection is effective.