Campus Units

Civil, Construction and Environmental Engineering, Electrical and Computer Engineering, Institute for Transportation

Document Type

Conference Proceeding


21st International Conference on Intelligent Transportation Systems (ITSC)

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Accepted Manuscript

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Journal or Book Title

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Research Focus Area

Transportation Engineering



Conference Title

21st International Conference on Intelligent Transportation Systems (ITSC)

Conference Date

November 4-7, 2018


Maui, HI


Early detection of incidents is a key step to reduce incident related congestion. State Department of Transportation (DoTs) usually install a large number of Close Circuit Television (CCTV) cameras in freeways for traffic surveillance. In this study, we used semi-supervised techniques to detect traffic incident trajectories from the cameras. Vehicle trajectories are identified from the cameras using state-of-the-art deep learning based You Look Only Once (YOLOv3) classifier and Simple Online Realtime Tracking (SORT) is used for vehicle tracking. Our proposed approach for trajectory classification is based on semi-supervised parameter estimation using maximum-likelihood (ML) estimation. The ML based Contrastive Pessimistic Likelihood Estimation (CPLE) attempts to identify incident trajectories from the normal trajectories. We compared the performance of CPLE algorithm to traditional semi-supervised techniques Self Learning and Label Spreading, and also to the classification based on the corresponding supervised algorithm. Results show that approximately 14% improvement in trajectory classification can be achieved using the proposed approach.


This is a manuscript of a proceeding published as Chakraborty, Pranamesh, Anuj Sharma, and Chinmay Hegde. "Freeway traffic incident detection from cameras: A semi-supervised learning approach." In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), (2018):1840-1845. DOI: 10.1109/ITSC.2018.8569426. Posted with permission.


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