International Journal of Computer Vision
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hybrid Biometric Person Authentication Using Face and Voice Features
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
A Graphical Model for Audiovisual Object Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Multi-View Classifier Swarms for Pedestrian Detection and Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Tracking Humans using Multi-modal Fusion
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Joint audio-visual tracking using particle filters
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Pedestrian tracking by fusion of thermal-visible surveillance videos
Machine Vision and Applications
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
A Swarm Intelligence inspired algorithm for contour detection in images
Applied Soft Computing
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This paper presents a new method for three dimensional object tracking by fusing information from stereo vision and stereo audio. From the audio data, directional information about an object is extracted by the Generalized Cross Correlation (GCC) and the object's position in the video data is detected using the Continuously Adaptive Mean shift (CAMshift) method. The obtained localization estimates combined with confidence measurements are then fused to track an object utilizing Particle Swarm Optimization (PSO). In our approach the particles move in the 3D space and iteratively evaluate their current position with regard to the localization estimates of the audio and video module and their confidences, which facilitates the direct determination of the object's three dimensional position. This technique has low computational complexity and its tracking performance is independent of any kind of model, statistics, or assumptions, contrary to classical methods. The introduction of confidence measurements further increases the robustness and reliability of the entire tracking system and allows an adaptive and dynamical information fusion of heterogenous sensor information.