2023
doi: 10.12720/jait.14.6.1261-1272
Observation-Centric with Appearance Metric for Computer Vision-Based Vehicle Counting
Allysa Kate Brillantes1,2,*
,
Edwin Sybingco1,2,3 ,
Robert Kerwin Billones2,3,4 ,
Argel Bandala1,2 ,
Alexis Fillone2,3,5 ,
Elmer Dadios2,3,4
- 1Department of Electronics and Computer Engineering, De La Salle University, Manila, Philippines
- 2Center for Engineering and Sustainable Development Research, Manila, Philippines
- 3Intelligent Systems Innovation (ISI, Inc.), Philippines
- 4Department of Manufacturing Engineering and Management, De La Salle University, Manila, Philippines
- 5Department of Civil Engineering, De La Salle University, Manila, Philippines
- Manuscript receivedApril 13, 2023
- revisedJune 19, 2023
- acceptedJuly 19, 2023
- publishedNovember 24, 2023
Abstract
Tracking objects in video sequences is a key step for applications involving computer vision like traffic monitoring and security systems. Occlusion is a frequent problem in object tracking which can result in the tracker losing track of the occluded object or misidentifying it with the occluding object. Moreover, the limited memory and computing power of traffic analysis systems presents a scaling problem, especially in object tracking applications. This paper aims to improve object tracking performance by minimizing data association errors in low frame rate tracking applications. Reducing frame rates alleviates memory and computing power limitations, and utilizing a tracker that can handle occlusion can address occlusion-related issues in object tracking. The proposed tracking method, Mask-OCSORT, uses the observation-tracking method with cosine similarity, intersection-over-union, and velocity consistency metrics for the association problem. The paper analyzes the effect of using bounding box and mask predictions of deep learning models in generating tracks. This study uses evaluation metrics like HOTA, MOTA, and IDF1 to assess the proposed tracking method, and employs evaluation metrics such as precision, recall, and F-score to assess the counting based on generated IDs from the tracking method. The study applied the Mask-OCSORT tracking for vehicle counting application and achieved an F-score of 87.18% at 5 frames per second (fps), and 75% at 1 fps.
Keywords
- mask-OCSORT
- object tracking
- instance segmentation
- traffic surveillance systems
- low framerate
- data association
How to Cite
Allysa Kate Brillantes, Edwin Sybingco, Robert Kerwin Billones, Argel Bandala, Alexis Fillone, and Elmer Dadios, "Observation-Centric with Appearance Metric for Computer Vision-Based Vehicle Counting," Journal of Advances in Information Technology, vol. 14, no. 6, pp. 1261-1272, 2023. https://doi.org/10.12720/jait.14.6.1261-1272
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
