Campus Units

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

Document Type

Conference Proceeding

Conference

21st International Conference on Intelligent Transportation Systems (ITSC)

Publication Version

Accepted Manuscript

Link to Published Version

https://doi.org/10.1109/ITSC.2018.8569426

Publication Date

2018

Journal or Book Title

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

First Page

1840

Last Page

1845

Research Focus Area

Transportation Engineering

DOI

10.1109/ITSC.2018.8569426

Conference Title

21st International Conference on Intelligent Transportation Systems (ITSC)

Conference Date

November 4-7, 2018

City

Maui, HI

Abstract

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.

Comments

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.

Rights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

Share

Article Location

 
COinS