Sensor models and multisensor integration
International Journal of Robotics Research - Special Issue on Sensor Data Fusion
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Likelihood Map Fusion for Visual Object Tracking
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
Quality-Based Fusion of Multiple Video Sensors for Video Surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fusing multiple video sensors for surveillance
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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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.