Camera/Vision for Geo-Location & Geo-Identification John S. Zelek Intelligent Human Machine Interface Lab Dept. of Systems Design Engineering University of Waterloo
Why can’t we use GPS everywhere? Urban canyons Indoor navigation 1. Introduction - 2/20
What we are trying to do Camera Inertial Altimeter, Compass +/- GPS = Accuracy + Location + Maps + 1. Introduction – 3/20
Applications 1. Introduction – 4/20
SLAM Given: Dead-reck. Ext. sensor Waypoints Not Known: Map GPS 2. SLAM – 5/20
Trees as landmarks for triangulation 2. SLAM - 6/20
Daniel Asmar Slide 7 Differentiating different trees 2. SLAM – 7/20
2. SLAM – 8/20
Object Category Recognition 3. Object Detection & Recognition – 9/20
Classes of Objects vs. Instances 2 instances of an individual object (space shuttle) 2 instances of an object face class 2 instances of an object motorcycle class 3. Object Detection & Recognition – 10/20
Visual vs. Functional classes There is a wide variation in the appearance of objects that are categorized by function. We focus only on categories related by some visual consistency only! 3. Object Detection & Recognition – 11/20
Challenges changes of viewpoint transformation (translation, rotation, scaling, affine), out-of-plane (foreshortening) illumination differences background clutter occlusion intra-class variation 3. Object Detection & Recognition – 12/20
Ours Others Repeatability of our detector appears to be better! 3. Object Detection & Recognition – 13/20
Object Graphs 3. Object Detection & Recognition – 14/20
3. Object Detection & Recognition – 15/20
3. Object Detection & Recognition – 16/20
4. Structure from Stereo – 17/20 Structure from stereo
Structure from motion 4. Structure From Motion – 18/20
5. Context Recognition – 19/20
6. Closing – 20/20
Extra. Features for Recognition & Structure – 21/20
Extra. Features for Recognition & Structure – 22/20