A Vision-Based Pipeline for Vehicle Counting, Speed Estimation, and Classification

Chenghuan Liu, Du Huynh, Chao Sun, Mark Reynolds, Steve Atkinson

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


Cameras have been widely used in traffic operations. While many technologically smart camera solutions in the market can be integrated into Intelligent Transport Systems (ITS) for automated detection, monitoring and data generation, many Network Operations (a.k.a Traffic Control) Centres still use legacy camera systems as manual surveillance devices. In this paper, we demonstrate effective use of these older assets by applying computer vision techniques to extract traffic data from videos captured by legacy cameras. In our proposed vision-based pipeline, we adopt recent state-of-the-art object detectors and transfer-learning to detect vehicles, pedestrians, and cyclists from monocular videos. By weakly calibrating the camera, we demonstrate a novel application of the image-to-world homography which gives our monocular vision system the efficacy of counting vehicles by lane and estimating vehicle length and speed in real-world units. Our pipeline also includes a module which combines a convolutional neural network (CNN) classifier with projective geometry information to classify vehicles. We have tested it on videos captured at several sites with different traffic flow conditions and compared the results with the data collected by piezoelectric sensors. Our experimental results show that the proposed pipeline can process 60 frames per second for pre-recorded videos and yield high-quality metadata for further traffic analysis.
Original languageEnglish
Pages (from-to)7547-7560
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number12
Early online date2020
Publication statusPublished - 1 Dec 2021


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