Title | ||
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Tiny-PIRATE: A Tiny model with Parallelized Intelligence for Real-time Analysis as a Traffic countEr |
Abstract | ||
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Due to the rapid growth in the number of vehicles over the last decade, there has been a dramatic increase in demand for highway capacity analysis. Vehicle counting, in particular, has become a key element of vision-based intelligent traffic systems deployed across metropolitan areas. Most methods solved the vehicle counting problem under the assumption of state-of-the-art computing systems. However, large-scale deployment of such systems for multi-camera processing is very inefficient. With the recent advancement of cost-efficient Internet-of-Things (IoT) devices alongside machine learning methods developed specifically for such devices, solving the vehicle counting problem for real-time traffic analysis on IoT edge devices, and thereby facilitating its large-scale deployment have become highly favorable. In this paper, we propose a framework of vehicle counting designed specifically for IoT edge computers which follows the detection-tracking-counting (DTC) model. The proposed solution aims at addressing the multimodality of contextual dynamics in traffic scenes with a small detector model, a robust tracker and a counting process that accurately estimate both a vehicle's motion of interest and its exit time from observation areas. Experimental results on AI City 2021 Track-1 Dataset showed that ours outperformed related methods with promising results regarding both accuracy and execution speed. |
Year | DOI | Venue |
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2021 | 10.1109/CVPRW53098.2021.00465 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGITION WORKSHOPS (CVPRW 2021) |
DocType | ISSN | Citations |
Conference | 2160-7508 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Synh Viet-Uyen Ha | 1 | 6 | 1.18 |
Nhat Minh Chung | 2 | 0 | 1.69 |
Cuong Tien Nguyen | 3 | 0 | 0.68 |
Hung Ngoc Phan | 4 | 0 | 2.03 |