12 March 1999 Using hidden Markov models to track human targets
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Abstract
This paper presents a multiple sensor approach to tracking mobile human targets. The goal of this research is to have a video camera automatically monitor a moving human subject in an environment that may contain multiple subjects and clutter. Real-time range data, obtained from arrays of acoustic sensor, are input to a hidden Markov model (HMM) and are processed in order to predict target location. The problem amounts to one of solving for and maximizing P(O/λ), which is the probability of obtaining an observation sequence O, given a HMM λ. First, the probability is calculated using the forward-backward recursive algorithm. Second, the parameters of the HMM are optimized using Baum-Welch iteration to maximize P(O/λ). The maximization procedure ceases when an acceptable tolerance, consistent with obtaining accurate prediction probabilities, is reached. Target track is extracted from the model using the Viterbi algorithm. The hidden Markov models were formulated analytically and were initially trained and tested using synthetic data. Results obtained for single human targets moving at random in a large room yield a close correlation between the HMM output and the actual target tracks.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Scott R. Thompson, Neil F. Chamberlain, Satyanarayana V. Parimi, "Using hidden Markov models to track human targets", Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (12 March 1999); doi: 10.1117/12.341361; https://doi.org/10.1117/12.341361
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