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Semantic trajectory-based event detection and event pattern mining

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Abstract

Video event detection is an effective way to automatically understand the semantic content of the video. However, due to the mismatch between low-level visual features and high-level semantics, the research of video event detection encounters a number of challenges, such as how to extract the suitable information from video, how to represent the event, how to build up reasoning mechanism to infer the event according to video information. In this paper, we propose a novel event detection method. The method detects the video event based on the semantic trajectory, which is a high-level semantic description of the moving object’s trajectory in the video. The proposed method consists of three phases to transform low-level visual features to middle-level raw trajectory information and then to high-level semantic trajectory information. Event reasoning is then carried out with the assistance of semantic trajectory information and background knowledge. Additionally, to release the users’ burden in manual event definition, a method is further proposed to automatically discover the event-related semantic trajectory pattern from the sample semantic trajectories. Furthermore, in order to effectively use the discovered semantic trajectory patterns, the associative classification-based event detection framework is adopted to discover the possibly occurred event. Empirical studies show our methods can effectively and efficiently detect video events.

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Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xiaofeng Wang, Guang Jiang & Zhongzhi Shi

  2. School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC, 3125, Australia

    Gang Li

Authors
  1. Xiaofeng Wang
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  2. Gang Li
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  3. Guang Jiang
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  4. Zhongzhi Shi
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Correspondence to Gang Li.

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Wang, X., Li, G., Jiang, G. et al. Semantic trajectory-based event detection and event pattern mining. Knowl Inf Syst 37, 305–329 (2013). https://doi.org/10.1007/s10115-011-0471-8

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  • DOI: https://doi.org/10.1007/s10115-011-0471-8

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