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Video events recognition by improved stochastic parsing based on extended stochastic context-free grammar representation
  • 时间:0
  • 分类:TP391[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构:[1]College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266510, China, [2]Beijing Lab of Intelligent Information Technology, Be]jing Institute of Technology, Beijing 100081, China
  • 相关基金:Supported by the National Natural Science Foundation of Chi- na (60805028, 60903146 ); Natural Science Foundation of Shandong Province of China ( ZR2010FM027 ) ; SDUST Re- search Fund ( 2010KYTD101 ) ; China Postdoctoral Science Foundation (2012M521336)
中文摘要:

Video events recognition is a challenging task for high-level understanding of video sequence. At present,there are two major limitations in existing methods for events recognition. One is that no algorithms are available to recognize events which happen alternately. The other is that the temporal relationship between atomic actions is not fully utilized. Aiming at these problems,an algorithm based on an extended stochastic contextfree grammar ( SCFG) representation is proposed for events recognition. Events are modeled by a series of atomic actions and represented by an extended SCFG. The extended SCFG can express the hierarchical structure of the events and the temporal relationship between the atomic actions. In comparison with previous work,the main contributions of this paper are as follows: ① Events ( include alternating events) can be recognized by an improved stochastic parsing and shortest path finding algorithm. ② The algorithm can disambiguate the detection results of atomic actions by event context. Experimental results show that the proposed algorithm can recognize events accurately and most atomic action detection errors can be corrected simultaneously.

英文摘要:

Video events recognition is a challenging task for high-level understanding of video se- quence. At present, there are two major limitations in existing methods for events recognition. One is that no algorithms are available to recognize events which happen alternately. The other is that the temporal relationship between atomic actions is not fully utilized. Aiming at these problems, an algo- rithm based on an extended stochastic context-free grammar (SCFG) representation is proposed for events recognition. Events are modeled by a series of atomic actions and represented by an extended SCFG. The extended SCFG can express the hierarchical structure of the events and the temporal re- lationship between the atomic actions. In comparison with previous work, the main contributions of this paper are as follows: ① Events (include alternating events) can be recognized by an improved stochastic parsing and shortest path finding algorithm. ② The algorithm can disambiguate the detec- tion results of atomic actions by event context. Experimental results show that the proposed algo- rithm can recognize events accurately and most atomic action detection errors can be corrected sim- ultaneously.

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