WANG Shengsheng, WEN Changji, LAI Yong, et al., “Interactive Activity Learning from Trajectories with Qualitative Spatio-Temporal Relation,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 508-512, 2015, doi: 10.1049/cje.2015.07.012
Citation: WANG Shengsheng, WEN Changji, LAI Yong, et al., “Interactive Activity Learning from Trajectories with Qualitative Spatio-Temporal Relation,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 508-512, 2015, doi: 10.1049/cje.2015.07.012

Interactive Activity Learning from Trajectories with Qualitative Spatio-Temporal Relation

doi: 10.1049/cje.2015.07.012
Funds:  This work is supported by the National Natural Science Foundation of China (No.61472161, No.61133011, No.61303132, No.61402195, No.61103091, No.61202308), Science Technology Development Project of Jilin Province (No.20140101201JC, No.201201131), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China and the Outstanding Youth Science Foundation of Jilin University.
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  • Corresponding author: LAI Yong (corresponding author) was born Jiangxi Province, in 1985. He received his Ph.D. degree from Jilin University in 2013. He is now a lecturer at the same university. His research interests are in the area of knowledge representation and reasoning. (Email:
  • Received Date: 2013-12-09
  • Rev Recd Date: 2014-09-24
  • Publish Date: 2015-07-10
  • Automatically analyzing interactions from video has gained much attention in recent years. Here a novel method has been proposed for analyzing interactions between two agents based on the trajectories. Previous works related to this topic are methods based on features, since they only extract features from objects. A method based on qualitative spatio-temporal relations is adopted which utilizes knowledge of the model (qualitative spatio-temporal relation calculi) instead of the original trajectory information. Based on the previous qualitative spatio-temporal relation works, such as Qualitative trajectory calculus (QTC), some new calculi are now proposed for long term and complex interactions. By the experiments, the results showed that our proposed calculi are very useful for representing interactions and improved the interaction learning more effectively.
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