Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks
Foreground detection plays an important role in the traffic surveillance applications, especially in urban intersections. Background subtraction is an efficient approach to segment the background and foreground with static cameras from video sensor networks. But when modelling the background, most s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2014-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/576759 |
| _version_ | 1797725271733829632 |
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| author | Rong Ding Shunli Wang Xu Liu |
| author_facet | Rong Ding Shunli Wang Xu Liu |
| author_sort | Rong Ding |
| collection | DOAJ |
| description | Foreground detection plays an important role in the traffic surveillance applications, especially in urban intersections. Background subtraction is an efficient approach to segment the background and foreground with static cameras from video sensor networks. But when modelling the background, most statistical techniques adjust the learning rate only based on the changes from video sequences, which is a crucial parameter controlling the updating speed. This causes a slow adaptation to sudden environmental changes. For example, a stopped car fuses into background before moving again, and it lowers the segmentation performance. This paper proposes an efficient way to address the problem by accounting for the physical world signal in traffic junctions. It assigns an adaptive learning rate to each pixel by integrating traffic light signal obtained from sensor networks. Combined with abundant physical world signals, background subtraction method is able to adapt itself to the outside world changes instantly. We test our approach in real urban traffic intersection; experimental results show that the new method increases the accuracy of detection and has a promising future. |
| first_indexed | 2024-03-12T10:28:46Z |
| format | Article |
| id | doaj.art-74547e9103c3431983fb1b80e25410e7 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T10:28:46Z |
| publishDate | 2014-04-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-74547e9103c3431983fb1b80e25410e72023-09-02T09:24:59ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772014-04-011010.1155/2014/576759576759Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor NetworksRong DingShunli WangXu LiuForeground detection plays an important role in the traffic surveillance applications, especially in urban intersections. Background subtraction is an efficient approach to segment the background and foreground with static cameras from video sensor networks. But when modelling the background, most statistical techniques adjust the learning rate only based on the changes from video sequences, which is a crucial parameter controlling the updating speed. This causes a slow adaptation to sudden environmental changes. For example, a stopped car fuses into background before moving again, and it lowers the segmentation performance. This paper proposes an efficient way to address the problem by accounting for the physical world signal in traffic junctions. It assigns an adaptive learning rate to each pixel by integrating traffic light signal obtained from sensor networks. Combined with abundant physical world signals, background subtraction method is able to adapt itself to the outside world changes instantly. We test our approach in real urban traffic intersection; experimental results show that the new method increases the accuracy of detection and has a promising future.https://doi.org/10.1155/2014/576759 |
| spellingShingle | Rong Ding Shunli Wang Xu Liu Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks International Journal of Distributed Sensor Networks |
| title | Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks |
| title_full | Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks |
| title_fullStr | Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks |
| title_full_unstemmed | Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks |
| title_short | Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks |
| title_sort | using traffic light signal to enhance intersection foreground detection based on video sensor networks |
| url | https://doi.org/10.1155/2014/576759 |
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