A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were gen...A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated.In addition,a fuzzy c-means(FCM)based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories.The algorithm can be further used to describe activities and identify anomalies.The experiments on two real scenes show that the algorithm is effective.展开更多
User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding ...User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.展开更多
GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning.Topic modeling is an effective tool to extract semantic information from taxi trajectory data.Howeve...GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning.Topic modeling is an effective tool to extract semantic information from taxi trajectory data.However,previous methods generally ignore trajectory directions that are important in the analysis of movement patterns.In this paper,we employ the bigram topic model rather than traditional topic models to analyze textualized trajectories and consider the direction information of trajectories.We further propose a modified Apriori algorithm to extract topical sub-trajectories and use them to represent each topic.Finally,we design a visual analytics system with several linked views to facilitate users to interactively explore movement patterns from topics and topical sub-trajectories.The case studies with Chengdu taxi trajectory data demonstrate the effectiveness of the proposed system.展开更多
基金National High-Tech Research and Development Plan of China(No.2003AA1Z2130)Science and Technology Project of Zhejiang Province of China(No.2005C1100102)
文摘A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated.In addition,a fuzzy c-means(FCM)based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories.The algorithm can be further used to describe activities and identify anomalies.The experiments on two real scenes show that the algorithm is effective.
文摘User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.
基金supported by the National Key Research&Development Program of China(2017YFB0202203)National Nat-ural Science Foundation of China(61472354,61672452)NSFCGuangdong Joint Fund,China(U1611263).
文摘GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning.Topic modeling is an effective tool to extract semantic information from taxi trajectory data.However,previous methods generally ignore trajectory directions that are important in the analysis of movement patterns.In this paper,we employ the bigram topic model rather than traditional topic models to analyze textualized trajectories and consider the direction information of trajectories.We further propose a modified Apriori algorithm to extract topical sub-trajectories and use them to represent each topic.Finally,we design a visual analytics system with several linked views to facilitate users to interactively explore movement patterns from topics and topical sub-trajectories.The case studies with Chengdu taxi trajectory data demonstrate the effectiveness of the proposed system.
