Multi-modal object tracking in a network of audiovisual sensors
Lead Research Organisation:
Queen Mary University of London
Department Name: Sch of Electronic Eng & Computer Science
Abstract
The aim of this project is to develop a unified scheme cooperative multi-modal and multi-sensor tracking. The multi-sensor network will be composed of stereo microphones coupled with omni-directional and with pan-tilt-zoom cameras. Sound information will be used to discriminate ambiguous visual observations as well as to extend the coverage area of the sensors beyond the field of view of the cameras. Although single modality as well as multi-modality trackers have achieved some success, a number of important tracking issues remain open for enabling the adoption of these algorithms in real-world scenarios. Among these issues, three important inter-related problems will be addressed in this project, namely the definition of a generic and flexible feature representation for a target, a reliable mechanism to update the target model based on incoming observations, and a robust multi-sensor handover strategy. First, we will develop a robust and adaptive representation of objects based on their acoustical and visual attributes while moving across the network of heterogeneous sensors. Next, object models will be defined based on the observation that temporal representation of a target is expected to lie in a low-dimensional manifold in the high-dimensional multi-modal feature space. Finally, the object model will be used to control and guide the evolution of the target state in order to help intra-sensor occlusion handling and inter-sensor handover. To evaluate the tracking scheme, we will create a test corpus and its associated ground-truth data for use in the project as well as for distribution to the research community to facilitate comparisons.
People |
ORCID iD |
Andrea Cavallaro (Principal Investigator) |
Publications
Maggio E
(2007)
Adaptive Multifeature Tracking in a Particle Filtering Framework
in IEEE Transactions on Circuits and Systems for Video Technology
Regazzoni C
(2008)
Video Tracking in Complex Scenes for Surveillance Applications
in EURASIP Journal on Image and Video Processing
Kayumbi G
(2008)
Multiview Trajectory Mapping Using Homography with Lens Distortion Correction
in EURASIP Journal on Image and Video Processing
Huiyu Zhou
(2008)
Target Detection and Tracking With Heterogeneous Sensors
in IEEE Journal of Selected Topics in Signal Processing
Maggio E
(2008)
Efficient Multitarget Visual Tracking Using Random Finite Sets
in IEEE Transactions on Circuits and Systems for Video Technology
Maggio E
(2009)
Accurate appearance-based Bayesian tracking for maneuvering targets
in Computer Vision and Image Understanding