| Autor: |
Hung Ngoc Phan, Long Hoang Pham, Synh Viet-Uyen Ha, Nhat Minh Chung, Tin Trung Thai |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
APCC |
| DOI: |
10.1109/apcc47188.2019.9026506 |
| Popis: |
Traffic surveillance system (TSS) is an essential tool to extract necessary information (count, type, speed, etc.) from cameras for traffic monitoring in many metro cities. In TSS, vehicle detection plays a pivotal role as it is a vital process for further analysis such as vehicle classification and vehicle tracking. So far there has been a considerable amount of research proposed with single-pipeline Convolution Neural Networks (CNN) to accommodate this subject. Although these studies achieved results with high accuracy, they required a large dataset and an implementation on dedicated hardware configuration. This paper presents a novel method with vision-based approach to detect moving vehicles from static surveillance cameras. Moving vehicles are detected and analysed by means of using a Neural Decision Tree accompanied with geometric features to classify vehicles and a Single Shot Detector to handle occlusion when inter-vehicle space between vehicles significantly decreases. Experiments have been conducted on the real-world data to evaluate the performance and accuracy of the proposed method. The results showed that our proposed method achieved a promising detection rate with real-time processing on regular hardware configuration. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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