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 ...
One challenge when tracking objects is to adapt the object representation depending on the scene context to account for changes in illumination, coloring, scaling, etc. Here, we p...
Ali Borji, Simone Frintrop, Dicky N. Sihite, Laure...
This paper presents a bottom-up approach that combines audio and video to simultaneously locate individual speakers in the video (2-D source localization) and segment their speech ...
We present a computational approach to abnormal visual event detection, which is based on exploring and modeling local motion patterns in a non-linear subspace. We use motion vect...
We describe how to create with machine learning techniques a generative, videorealistic, speech animation module. A human subject is first recorded using a videocamera as he/she u...