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The paper presents a fusion-tracker and pedestrian classifier for color and thermal cameras. The tracker builds a background model as a multi-modal distribution of colors and temperatures. It is constructed as a particle filter that makes a number of informed reversible transformations to sample the model probability space in order to maximize posterior probability of the scene model. Observation likelihoods of moving objects account their 3D locations with respect to the camera and occlusions by other tracked objects as well as static obstacles. After capturing the coordinates and dimensions of moving objects we apply a pedestrian classifier based on periodic gait analysis. To separate humans from other moving objects, such as cars, we detect,in human gait, a symmetrical double helical pattern, that can then be analyzed using the Frieze Group theory. The results of tracking on color and thermal sequences demonstrate that our algorithm is robust to illumination noise and performs well in the outdoor environments.

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