Vehicle Detection by Fusing Part Model Learning and Semantic Scene Information for Complex Urban Surveillance
Visual-based vehicle detection has been studied extensively, however there are great challenges in certain settings. To solve this problem, this paper proposes a probabilistic framework combining a scene model with a pattern recognition method for vehicle detection by a stationary camera. A semisupe...
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doaj-d5719b132a2048cf8bf6df544c5a3b0b2020-11-25T00:40:21ZengMDPI AGSensors1424-82202018-10-011810350510.3390/s18103505s18103505Vehicle Detection by Fusing Part Model Learning and Semantic Scene Information for Complex Urban SurveillanceYingfeng Cai0Ze Liu1Hai Wang2Xiaobo Chen3Long Chen4Automotive Engineering Research Institution, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaAutomotive Engineering Research Institution, Jiangsu University, Zhenjiang 212013, ChinaAutomotive Engineering Research Institution, Jiangsu University, Zhenjiang 212013, ChinaVisual-based vehicle detection has been studied extensively, however there are great challenges in certain settings. To solve this problem, this paper proposes a probabilistic framework combining a scene model with a pattern recognition method for vehicle detection by a stationary camera. A semisupervised viewpoint inference method is proposed in which five viewpoints are defined. For a specific monitoring scene, the vehicle motion pattern corresponding to road structures is obtained by using trajectory clustering through an offline procedure. Then, the possible vehicle location and the probability distribution around the viewpoint in a fixed location are calculated. For each viewpoint, the vehicle model described by a deformable part model (DPM) and a conditional random field (CRF) is learned. Scores of root and parts and their spatial configuration generated by the DPM are used to learn the CRF model. The occlusion states of vehicles are defined based on the visibility of their parts and considered as latent variables in the CRF. In the online procedure, the output of the CRF, which is considered as an adjusted vehicle detection result compared with the DPM, is combined with the probability of the apparent viewpoint in a location to give the final vehicle detection result. Quantitative experiments under a variety of traffic conditions have been contrasted to test our method. The experimental results illustrate that our method performs well and is able to deal with various vehicle viewpoints and shapes effectively. In particular, our approach performs well in complex traffic conditions with vehicle occlusion.http://www.mdpi.com/1424-8220/18/10/3505Vehicle detectiontraffic surveillancedeformable part model (DPM)conditional random field (CRF)context-based inference |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yingfeng Cai Ze Liu Hai Wang Xiaobo Chen Long Chen |
spellingShingle |
Yingfeng Cai Ze Liu Hai Wang Xiaobo Chen Long Chen Vehicle Detection by Fusing Part Model Learning and Semantic Scene Information for Complex Urban Surveillance Sensors Vehicle detection traffic surveillance deformable part model (DPM) conditional random field (CRF) context-based inference |
author_facet |
Yingfeng Cai Ze Liu Hai Wang Xiaobo Chen Long Chen |
author_sort |
Yingfeng Cai |
title |
Vehicle Detection by Fusing Part Model Learning and Semantic Scene Information for Complex Urban Surveillance |
title_short |
Vehicle Detection by Fusing Part Model Learning and Semantic Scene Information for Complex Urban Surveillance |
title_full |
Vehicle Detection by Fusing Part Model Learning and Semantic Scene Information for Complex Urban Surveillance |
title_fullStr |
Vehicle Detection by Fusing Part Model Learning and Semantic Scene Information for Complex Urban Surveillance |
title_full_unstemmed |
Vehicle Detection by Fusing Part Model Learning and Semantic Scene Information for Complex Urban Surveillance |
title_sort |
vehicle detection by fusing part model learning and semantic scene information for complex urban surveillance |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-10-01 |
description |
Visual-based vehicle detection has been studied extensively, however there are great challenges in certain settings. To solve this problem, this paper proposes a probabilistic framework combining a scene model with a pattern recognition method for vehicle detection by a stationary camera. A semisupervised viewpoint inference method is proposed in which five viewpoints are defined. For a specific monitoring scene, the vehicle motion pattern corresponding to road structures is obtained by using trajectory clustering through an offline procedure. Then, the possible vehicle location and the probability distribution around the viewpoint in a fixed location are calculated. For each viewpoint, the vehicle model described by a deformable part model (DPM) and a conditional random field (CRF) is learned. Scores of root and parts and their spatial configuration generated by the DPM are used to learn the CRF model. The occlusion states of vehicles are defined based on the visibility of their parts and considered as latent variables in the CRF. In the online procedure, the output of the CRF, which is considered as an adjusted vehicle detection result compared with the DPM, is combined with the probability of the apparent viewpoint in a location to give the final vehicle detection result. Quantitative experiments under a variety of traffic conditions have been contrasted to test our method. The experimental results illustrate that our method performs well and is able to deal with various vehicle viewpoints and shapes effectively. In particular, our approach performs well in complex traffic conditions with vehicle occlusion. |
topic |
Vehicle detection traffic surveillance deformable part model (DPM) conditional random field (CRF) context-based inference |
url |
http://www.mdpi.com/1424-8220/18/10/3505 |
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