{"type":"ScholarlyArticle","title":"Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information","authors":[{"name":"Yang Wang","slug":"yang-wang","given":"Yang","family":"Wang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; School of Software Engineering , University of Science and Technology of China"},{"name":"Yiwei Xiao","slug":"yiwei-xiao","given":"Yiwei","family":"Xiao","affiliation":"School of Software Engineering , University of Science and Technology of China"},{"name":"Xike Xie","slug":"xike-xie","given":"Xike","family":"Xie","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Ruoyu Chen","slug":"ruoyu-chen","given":"Ruoyu","family":"Chen","affiliation":"School of Software Engineering , University of Science and Technology of China"},{"name":"Hengchang Liu","slug":"hengchang-liu","given":"Hengchang","family":"Liu","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"}],"abstract":"Recent advances in  video surveillance systems enable a new paradigm for intelligent urban traffic management systems. Since surveillance cameras are usually sparsely located to cover key regions of the road under surveillance, it is a big challenge to perform a complete real-time traffic pattern analysis based on incomplete sparse surveillance information. As a result, existing works mostly focus on predicting traffic volumes with historical records available at a particular location  and may not provide a complete picture of real-time traffic patterns. To this end, in this paper, we go beyond existing works and tackle the challenges of traffic flow analysis from three perspectives. First, we train the transition probabilities to capture vehicles' movement patterns. The transition probabilities are trained from third-party vehicle GPS data, and thus can work in the area even if there is no camera. Second, we exploit the Multivariate Normal Distribution model together with the transferred probabilities to estimate the unobserved traffic patterns. Third, we propose an algorithm for real-time traffic inference with  surveillance as a complement source of information. Finally, experiments on real-world data show the effectiveness of our approach.","doi":"10.24963/ijcai.2018/496","url":"https://ijcai.org/proceedings/2018/496","pdf":"https://ijcai.org/proceedings/2018/0496.pdf","markdown":"https://ijcai.org/proceedings/2018/496.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/496","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":496,"pages":{"first":3571,"last":3577},"keywords":["Machine Learning Applications: Other Applications","Uncertainty in AI: Approximate Probabilistic Inference","Machine Learning Applications: Big data ; Scalability","Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning"],"videos":[],"references":[{"position":1,"raw":"[Bas et al., 2007] Erhan Bas, A Murat Tekalp, and F Sibel Salman. 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