About: In this chapter, we present a probabilistic reasoning approach to recognizing people entering and leaving a closed room by exploiting low-level visual features and high-level domain-specific knowledge. Specifically, people in the view of a monitoring camera are first detected and tracked so that their color and facial features can be extracted and analyzed. Then, recognition of people is carried out using a mapped feature similarity measure and exploiting the temporal correlation and constraints among each sequence of observations. The optimality of recognition is achieved in the sense of maximizing the joint posterior probability of the multiple observations. Experimental results of real and synthetic data are reported to show the effectiveness of the proposed approach.   Goto Sponge  NotDistinct  Permalink

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  • In this chapter, we present a probabilistic reasoning approach to recognizing people entering and leaving a closed room by exploiting low-level visual features and high-level domain-specific knowledge. Specifically, people in the view of a monitoring camera are first detected and tracked so that their color and facial features can be extracted and analyzed. Then, recognition of people is carried out using a mapped feature similarity measure and exploiting the temporal correlation and constraints among each sequence of observations. The optimality of recognition is achieved in the sense of maximizing the joint posterior probability of the multiple observations. Experimental results of real and synthetic data are reported to show the effectiveness of the proposed approach.
Subject
  • Dimensionless numbers
  • Non-classical logic
  • Similarity and distance measures
  • Probabilistic arguments
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