Distributed multi-class road user tracking in multi-camera network for smart traffic applications
| Autor: | Maarten Slembrouck, Peter Veelaert, Nyan Bo Bo, Wilfried Philips |
|---|---|
| Přispěvatelé: | Blanc-Talon, Jacques, Delmas, Patrice, Philips, Wilfried, Popescu, Dan, Scheunders, Paul |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Class (computer programming)
Technology and Engineering Computer science business.industry Node (networking) Real-time computing road user tracking Pedestrian 010501 environmental sciences Tracking (particle physics) Track (rail transport) 01 natural sciences smart camera network 010309 optics distributed computing Software deployment 0103 physical sciences Anomaly detection trajectory analysis Artificial intelligence Smart camera business road traffic statistics smart traffic 0105 earth and related environmental sciences |
| Zdroj: | Advanced concepts for intelligent vision systems-ACIVS 2020 Advanced Concepts for Intelligent Vision Systems ISBN: 9783030406042 ACIVS |
| ISSN: | 0302-9743 1611-3349 |
| Popis: | Reliable tracking of road users is one of the important tasks in smart traffic applications. In these applications, a network of cameras is often used to extend the coverage. However, efficient usage of information from cameras which observe the same road user from different view points is seldom explored. In this paper, we present a distributed multi-camera tracker which efficiently uses information from all cameras with overlapping views to accurately track various classes of road users. Our method is designed for deployment on smart camera networks so that most computer vision tasks are executed locally on smart cameras and only concise high-level information is sent to a fusion node for global joint tracking. We evaluate the performance of our tracker on a challenging real-world traffic dataset in an aspect of Turn Movement Count (TMC) application and achieves high accuracy of 93% and 83% on vehicles and cyclist respectively. Moreover, performance testing in anomaly detection shows that the proposed method provides reliable detection of abnormal vehicle and pedestrian trajectories. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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