Survey on contemporary remote surveillance systems for public safety
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Computer Vision and Image Understanding
Pedestrian counting in video sequences using optical flow clustering
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
A user-centric control and navigation for augmented virtual environment surveillance application
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Thermal cameras and applications: a survey
Machine Vision and Applications
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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 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.