Pedestrian tracking by fusion of thermal-visible surveillance videos

In this paper we introduce a system to track pedestrians using a combined input from RGB and thermal cameras. Two major contributions are presented here. First is the novel probabilistic model of the scene background where each pixel is represented as a multi-modal distribution with the changing number of modalities for both color and thermal input. We demonstrate how to eliminate the influence of shadows with this type of fusion. Second, based on our background model we introduce a pedestrian tracker designed as a particle filter. We further develop a number of informed reversible transformations to sample the model probability space in order to maximize our model posterior probability. The novelty of our tracking approach also comes from a way we formulate observation likelihoods to account for 3D locations of the bodies with respect to the camera and occlusions by other tracked human bodies as well as static objects. 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.