| Paper: | WA-L2.1 |
| Session: | Video Object Segmentation and Tracking II |
| Time: | Wednesday, September 19, 09:50 - 10:10 |
| Presentation: |
Lecture
|
| Title: |
MULTI-MODAL PARTICLE FILTERING TRACKING USING APPEARANCE, MOTION AND AUDIO LIKELIHOODS |
| Authors: |
Matteo Bregonzio; Queen Mary, University of London | | |
| | Murtaza Taj; Queen Mary, University of London | | |
| | Andrea Cavallaro; Queen Mary, University of London | | |
| Abstract: |
We propose a multi-modal object tracking algorithm that combines appearance, motion and audio information in a particle filter. The proposed tracker fuses at the likelihood level the audio-visual observations captured with a video camera coupled with two microphones. Two video likelihoods are computed that are based on a 3D color histogram appearance model and on a color change detection, whereas an audio likelihood provides information about the direction of arrival of a target. The direction of arrival is computed based on a multi-band generalized cross-correlation function enhanced with a noise suppression and reverberation filtering that uses the precedence effect. We evaluate the tracker on single and multi-modality tracking and quantify the performance improvement introduced by integrating audio and visual information in the tracking process. |
