Traffic behavior recognition using the pachinko allocation model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Huynh-The T. | - |
dc.contributor.author | Banos O. | - |
dc.contributor.author | Le B.-V. | - |
dc.contributor.author | Bui D.-M. | - |
dc.contributor.author | Yoon Y. | - |
dc.contributor.author | Lee S. | - |
dc.date.available | 2021-02-22T11:35:14Z | - |
dc.date.issued | 2015-07 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/10502 | - |
dc.description.abstract | CCTV-based behavior recognition systems have gained considerable attention in recent years in the transportation surveillance domain for identifying unusual patterns, such as traffic jams, accidents, dangerous driving and other abnormal behaviors. In this paper, a novel approach for traffic behavior modeling is presented for video-based road surveillance. The proposed system combines the pachinko allocation model (PAM) and support vector machine (SVM) for a hierarchical representation and identification of traffic behavior. A background subtraction technique using Gaussian mixture models (GMMs) and an object tracking mechanism based on Kalman filters are utilized to firstly construct the object trajectories. Then, the sparse features comprising the locations and directions of the moving objects are modeled by PAMinto traffic topics, namely activities and behaviors. As a key innovation, PAM captures not only the correlation among the activities, but also among the behaviors based on the arbitrary directed acyclic graph (DAG). The SVM classifier is then utilized on top to train and recognize the traffic activity and behavior. The proposed model shows more flexibility and greater expressive power than the commonly-used latent Dirichlet allocation (LDA) approach, leading to a higher recognition accuracy in the behavior classification. © 2015, by the authors; licensee MDPI, Basel, Switzerland. | - |
dc.format.extent | 20 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI AG | - |
dc.title | Traffic behavior recognition using the pachinko allocation model | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/s150716040 | - |
dc.identifier.scopusid | 2-s2.0-84936972450 | - |
dc.identifier.wosid | 000361788200059 | - |
dc.identifier.bibliographicCitation | Sensors, v.15, no.7, pp 16040 - 16059 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 15 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 16040 | - |
dc.citation.endPage | 16059 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | Directed graphs | - |
dc.subject.keywordPlus | Monitoring | - |
dc.subject.keywordPlus | Pattern recognition systems | - |
dc.subject.keywordPlus | Roads and streets | - |
dc.subject.keywordPlus | Security systems | - |
dc.subject.keywordPlus | Statistics | - |
dc.subject.keywordPlus | Support vector machines | - |
dc.subject.keywordPlus | Traffic congestion | - |
dc.subject.keywordPlus | Background subtraction techniques | - |
dc.subject.keywordPlus | Closed circuit television | - |
dc.subject.keywordPlus | Directed acyclic graph (DAG) | - |
dc.subject.keywordPlus | Gaussian mixture model (GMMs) | - |
dc.subject.keywordPlus | Hierarchical representation | - |
dc.subject.keywordPlus | Latent dirichlet allocations | - |
dc.subject.keywordPlus | Pachinko allocation models | - |
dc.subject.keywordPlus | Traffic behavior modeling | - |
dc.subject.keywordPlus | Behavioral research | - |
dc.subject.keywordPlus | automated pattern recognition | - |
dc.subject.keywordPlus | behavior | - |
dc.subject.keywordPlus | car driving | - |
dc.subject.keywordPlus | classification | - |
dc.subject.keywordPlus | human | - |
dc.subject.keywordPlus | image processing | - |
dc.subject.keywordPlus | procedures | - |
dc.subject.keywordPlus | statistical model | - |
dc.subject.keywordPlus | statistics and numerical data | - |
dc.subject.keywordPlus | videorecording | - |
dc.subject.keywordPlus | Automobile Driving | - |
dc.subject.keywordPlus | Behavior | - |
dc.subject.keywordPlus | Humans | - |
dc.subject.keywordPlus | Image Processing, Computer-Assisted | - |
dc.subject.keywordPlus | Models, Statistical | - |
dc.subject.keywordPlus | Pattern Recognition, Automated | - |
dc.subject.keywordPlus | Video Recording | - |
dc.subject.keywordAuthor | Closed-circuit television (CCTV) system | - |
dc.subject.keywordAuthor | Pachinko allocation model | - |
dc.subject.keywordAuthor | Traffic behavior modeling | - |
dc.subject.keywordAuthor | Video-based road surveillance | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/15/7/16040 | - |
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