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Automatic parsing of lane and road boundaries in challenging traffic scenes

[+] Author Affiliations
Mohamed A. Helala, Faisal Z. Qureshi, Ken Q. Pu

University of Ontario Institute of Technology, Faculty of Science, 2000 Simcoe Street North, Oshawa, Ontario L1H 7K4, Canada

J. Electron. Imaging. 24(5), 053020 (Oct 06, 2015). doi:10.1117/1.JEI.24.5.053020
History: Received November 28, 2014; Accepted August 28, 2015
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Abstract.  Automatic detection of road boundaries in traffic surveillance imagery can greatly aid subsequent traffic analysis tasks, such as vehicle flow, erratic driving, and stranded vehicles. This paper develops an online technique for identifying the dominant road boundary in video sequences captured by traffic cameras under challenging environmental and lighting conditions, e.g., unlit highways captured at night. The proposed method works in real time of up to 20frames/s and generates a ranked list of road regions that identify road and lane boundaries. Our method begins by segmenting each frame into a set of superpixels. An adaptive sampling step approximates superpixel contours to a collection of edge segments. Next, we show how online hierarchical clustering can be efficiently used to organize edges into clusters of colinearly similar sets. Promising clusters are paired with each other to form cluster pairs. Then we present and prove a statistical ranking measure that is used along with road-activity and perspective cues to find the dominant road boundaries. We evaluate the proposed approach on two real-world datasets to test our method under camera viewpoint changes and extreme environmental and lighting conditions. Results show that our method outperforms two state-of-the-art techniques in precision, recall, and runtime.

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Topics

Cameras ; Roads

Citation

Mohamed A. Helala ; Faisal Z. Qureshi and Ken Q. Pu
"Automatic parsing of lane and road boundaries in challenging traffic scenes", J. Electron. Imaging. 24(5), 053020 (Oct 06, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.5.053020


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