BayesianDecisionTheory_CaseStudies

BayesianDecisionTheory_CaseStudies - A bayesian approach to...

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A bayesian approach to human activity recognition (A. Madabhushi and J. Aggarwal, "A bayesian approach to human activity recognition", 2nd Inter- national Workshop on Visual Surveillance , pp. 25-30, June 1999 (hard-copy)) Human activity recognition - Recognize human actions using visual information. - One of the hottest problems in computer vision (see review section). - Applications include monitoring of human activity in department stores, air- ports, high-security buildings etc. - Building systems that can recognize any type of action is a difficult and chal- lenging problem. Goal - Build a system which is capable of recognizing the following 10 (ten) actions (from a frontal or lateral view): (1) sitting down (2) standing up (3) bending down (4) getting up (5) hugging (6) squatting (7) rising from a squatting position (8) bending sideways (9) falling backward (10) walking - The frontal and lateral views of each action are modeled as individual action sequences. - Input sequences are matched against stored models of actions. - The input sequence is identified by finding the closest stored action.
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-2- Approach - Human actions can be recognized by tracking various body parts. - People sit, stand, walk, bend down, and get up in a more or less similar fash- ion. - The head of a person moves in a characteristic fashion during these actions. - The movement of the head of the subject over consecutive frames is used to represent the above actions. - Recognition is formulated as Bayesian classification. Strengths and weaknesses - The system is able to recognize actions where the gait of the subject in the input sequence differs considerably from the training sequences. - It is also able to recognize actions for people of varying physical structure (i.e., tall, short, fat, thin etc.). - Only actions in the frontal or lateral view can be recognized successfully. - Some of the assumptions made might not be valid (we will discuss them as we proceed).
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-3- Representation scheme of actions - Estimate the centroid of the head in each frame: ( x 1 , y 1 ) , ( x 2 , y 2 ) , ..., ( x n + 1 , y n + 1 ) - Find the absolute value differences in successive frames: X = ( dx 1 , dx 2 , . . . , dx n ) Y = ( dy 1 ), dy 2 , . . . , dy n ) where dx i = x i + 1 - x i and dy i = y i + 1 - y i - This representation scheme provides invariance .... Head detection and tracking - The centroid of the head is tracked from frame to frame. - Accurate head detection and tracking are crucial. - Detection is done by hand in the current implementation.
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-4- Bayesian formulation - Giv en an input sequence, a posteriori probabilities are computed using each of the training actions.
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