Multi-sensor Multi-cue Fusion for Object Detection in Video Surveillance

We here present a multi-sensor data fusion architecture that takes into account the performance of video sensors in detecting moving targets for video surveillance purposes. Target detection and tracking is performed via classification by an ensemble of classifiers learned online using heterogeneous features for each target. A novel approach is then used to estimate the position of the target on the ground plane map by temporally fusing likelihood maps, then by approximating likelihoods analytically by a Gaussian function, and eventually projecting and fusing the likelihood functions. Experimental results are shown on real-world video sequences.