SBIR Phase I: Using Hidden Markov Models to Track Human Targets

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This Small Business Innovation Research Phase I project will investigate the feasibility Of implementing a novel multi-sensor data fusion technique for low cost target tracking. Literature suggests that Hidden Markov Models significantly out perform other methods of multiple target tracking when ambiguous detections, noise, and clutter are present. The primary objective here is to demonstrate the feasibility of utilizing Hidden Markov Models to track human targets. The target tracking problem can be viewed as one of developing a model that minimizes the difference between the model's estimate of the target coordinates {Xout(t), Yout(t)} and the actual coordinates given by {Xactual(t), Yactual(t)}. In practice, this must be done simultaneously for a varying number of independent targets. However, this Phase I effort will focus on single target tracking and leave multiple target tracking to the subsequent Phase II project. Portable code used to implement models in Phase I will be incorporated in a microprocessor-based system following multiple target refinement in Phase II. Synthesized sensor data will be used initially to develop tracking models. These data will then be supplemented with measured data from ultrasonic sensors, in order to further refine the tracking model. Direct applications of the human tracking system include video surveillance for security, safety, and human or animal behavioral observation systems. With spotlight attachments the device will provide an illuminated path for remote search and rescue operations, emergency evacuation in poorly lit buildings, crime deterrence, and night surveillance tracking of escaping prisoners. This system will also be well suited as a supplement to a video target tracking and identification system. This award is supported by the Small Business Innovation Research Program and the Experimental Program to Stimulate Competitive Research (EPSCoR).