Conference Paper

View-Invariant Human Action Detection Using Component-Wise HMM of Body Parts

DOI: 10.1007/978-3-540-70517-8_20 Conference: Articulated Motion and Deformable Objects, 5th International Conference, AMDO 2008, Port d'Andratx, Mallorca, Spain, July 9-11, 2008, Proceedings
Source: DBLP


This paper presents a framework for view-invariant action recognition in image sequences. Feature-based human detection becomes extremely chal- lenging when the agent is being observed from different viewpoints. Besides, similar actions, such as walking and jogging, are hardly distinguishable by con- sidering the human body as a whole. In this work, we have developed a system which detects human body parts under different views and recognize similar ac- tions by learning temporal changes of detected body part components. Firstly, human body part detection is achieved to find separately three components of the human body, namely the head, legs and arms. We incorporate a number of sub-classifiers, each for a specific range of view-point, to detect those body parts. Subsequently, we have extended this approach to distinguish and recognise ac- tions like walking and jogging based on component-wise HMM learning.

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Available from: Jordi Gonzàlez, Mar 27, 2014
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    • "It is of significant interest in many applications, such as automated surveillance, aerial video analysis, sport video annotation and search. Various visual cues have been shown to be effective for representing human actions, including motion [8] [9], contours [3] [12], extremities [22], and body parts [5] [18], etc. Most of these features can be reliably extracted from image sequences of medium to high-resolution. "
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