An Embedded Computer-Vision System for Multi-Object Detection in Traffic Surveillance
| Autor: | Thierry Chateau, Ala Mhalla, Najoua Essoukri Ben Amara, Sami Gazzah |
|---|---|
| Přispěvatelé: | Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Laboratory of Advanced Technology and Intelligent Systems (LATIS), Ecole Nationale d'Ingénieurs de Sousse (ENISo) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
Artificial neural network Computer science Embedded computer vision Mechanical Engineering Visual texture recognition 05 social sciences Real-time computing Detector [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Object detection Computer Science Applications 0502 economics and business Automotive Engineering Specialization (functional) Surveillance and monitoring Intelligent transportation system ComputingMilieux_MISCELLANEOUS |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems IEEE Transactions on Intelligent Transportation Systems, IEEE, 2018, pp.1-13. ⟨10.1109/TITS.2018.2876614⟩ |
| ISSN: | 1524-9050 |
| DOI: | 10.1109/TITS.2018.2876614⟩ |
| Popis: | Intelligent traffic systems for traffic surveillance and monitoring have become a topic of great interest to some cities in the world. Generally, the existing traffic surveillance systems are made up of costly equipment with complicated operational procedures and have difficulties with congestion, occlusion, and lighting night/day and day/night transitions. In this paper, we propose an embedded system for traffic surveillance that can be utilized under these challenging conditions. This system analyses traffic and particularly focuses on the problem of detecting and categorizing traffic objects in several traffic scenarios. Moreover, it contains a robust detector produced by an original specialization framework. The proposed specialization framework utilizes a generic deep detector so as to improve the detection accuracy in a specific traffic scenario. The experiments demonstrate that the proposed specialization framework presents encouraging results for multi-traffic object detection and outperforms the state-of-the-art specialization frameworks on several public traffic datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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