Particle filtering with multiple and heterogeneous cameras

This work proposes a novel particle filter for tracking multiple people using multiple and heterogeneous cameras, namely monocular and stereo cameras. Our approach is to define confidence models and observation models for each type of camera. Particles are evaluated independently in each camera, and then the data are fused in accordance with the confidence. Confidence models take into account several sources of information. On the one hand, they consider occlusion information from an occlusion map calculated using a depth-ordered particle evaluation. On the other hand, the relative precision of sensors is considered so that the contribution of a sensor in the final data fusion step is proportional to its precision. We have defined confidence and observation models for monocular and stereo cameras and have designed tests to validate our proposal. The experiments show that our method is able to operate with each type individually and in combination. Two other remarkable properties of our method are that it is highly parallelizable and that it does not impose restrictions on the cameras’ positions or orientations.