K.U. K.U. & Leuven & Leuven 2 Computer Vision Labs Prof. Luc Van Gool ETH - Switzerland Un. Leuven Belgium appr. 15 researchers Tracking Recognition Recognition Passive 3D Active 3D Hum.-comp. interact. K.U. K.U. & Leuven & Leuven An overview of some vision trends scene reconstruction recognition tracking K.U. K.U. & Leuven & Leuven Scene reconstruction 3D acquisition with off-the-shelf HW 4D capture: dynamic 3D Realistic texture synthesis City modelling Intuitive visualisation K.U. K.U. & Leuven & Leuven One-shot ShapeCam 3D acquisition K.U. K.U. & Leuven & Leuven 3D acquisition K.U. K.U. & Leuven & Leuven 3D from hand-held camera images K.U. K.U. & Leuven & Leuven... The result generated by ARC3D K.U. K.U. & Leuven & Leuven 4D acquisition 3D snapshots in fast succession K.U. K.U. & Leuven & Leuven Shape-from-silhouettes K.U. K.U. & Leuven & Leuven Outdoor visual hulls K.U. K.U. & Leuven & Leuven Realistic texturing Stochastic & hierarchical texture models Viewpoint/illumination dependent texture Minidome: portable photometric stereo K.U. K.U. & Leuven & Leuven IKT Realistic texture Given examples Synthetic textures K.U. K.U. & Leuven & Leuven AUTOMATIC Realistic texture K.U. K.U. & Leuven & Leuven Recognition K.U. K.U. & Leuven & Leuven Object recognition Independent of viewpoint Irrespective of occlusion In the presence of scene clutter Under variable illumination Robust against deformations Latest techniques based on `invariant regions K.U. K.U. & Leuven & Leuven The ellipses show invariant regions, they cover the same part of the scene The crux is that they were found independently Object recognition K.U. K.U. & Leuven & Leuven Object recognition Example application: automatic annotation of video data E.g. finding same object somewhere else in a complete movie Searching for the van in `groundhog day K.U. K.U. & Leuven & Leuven Automatic retrieval of all scenes with the van based on the example image K.U. K.U. & Leuven & Leuven Object recognition Next challenge: categorisation i.e. not recognising particular objects, but rather the class an object belongs to, e.g. a car, a person, etc. This is more difficult, because of the Intra-class variability K.U. K.U. & Leuven & Leuven Object recognition Next challenge: categorisation K.U. K.U. & Leuven & Leuven Finding people Security / surveillance / annotation e.g. pedestrian detector K.U. K.U. & Leuven & Leuven Tracking Robust blob tracking anti-drift Body pose tracking Detailed hand tracking Action recognition Gait analysis K.U. K.U. & Leuven & Leuven Multi-feature tracker K.U. K.U. & Leuven & Leuven HandyMouse project Skin color Detection, Tracking, and Gesture analysis For Minority Report style interaction K.U. K.U. & Leuven & Leuven Marker-less motion capture K.U. K.U. & Leuven & Leuven Spin-offs / start-ups 1. ICOS 2. Eyetronics 3. GeoAutomation 4. eSaturnus 5. Kooaba 6. Procedural