1. Benny Neeman Leon Ribinik 27/01/2009

2. Our Goal – People Tracking We would like to be able to track and distinguish the different people in a movie.

3. Why?  For security purposes.  Collecting statistics – e.g. “How many people visit the mall each day?”.  Much more…

4. Our Approach  Most tracking algorithms try to track the whole person.  We think heads are enough: One head per person. Heads are pretty much rigid. Easier to detect a whole head without losing parts of the blob. In occlusion of bodies the heads are mostly the last to be occluded.

5. Simplifying the Problem  Our algorithm is highly dependent on different factors: Shooting angle. People density in the movie. Camera distance from the scene. General stuff: lighting, noise, etc.  We can use parameters to handle different conditions conditions.

6. How do we do it?  First, we detect the foreground and the meaningful foreground contours.  Then, we detect the heads as the local maximum of the foreground contours.  The local maximum’s contour is approximately the ellipse representing the head.

7. How do we do it?(2)  The contour of the neck changes its curvature when touching the shoulders this fact can be used to avoid false positives.  We use trajectories to trace heads between frames.

8. How do we do it?(3)  To avoid false negatives (actual heads not being detected) we use hysteresis with two thresholds.  The center of the detected head ellipse detected is tested to be part of the foreground.

9. Parameters  Background threshold.  Hysteresis threshold.  Maximum head distance between frames.  Parameters for defining head size.  Parameter to define trajectory memory.  More parameters…

10. Difficulties We’ve Encountered  Getting good enough foreground contours to start working with. The contours of the edge detector are more accurate but are not continuous.  Some heads overlap the foreground so their contour is not part of the foreground - false negative.  Algorithm mistakes other body parts for heads – false positive.

11. Our Limitations

13. If We Had Another Semester…  Combining contours from edge detector to get more accurate edges and find heads which don’t belong to the foreground contour.  Finding a transition between the heads in multiple cameras.  Improving the algorithm so it’d work on more general input.

14. Demo

15. Summary  Combining numerous methods, we’ve managed to obtain a reasonable solution on a limited set of examples.  Expanding the algorithm to support generic input would require a lot of work and more methods.