Exploring Context to Learn Scene Specific Object Detectors

S. Stalder, H. Grabner, and L. Van Gool
In Proceedings CVPR09 Workshop on Performance Evaluation of Tracking and Surveillance (PETS), 2009 [pdf] [bibtex]
Download presentation slides [pdf] [ppt]

Generic person detection is an ill-posed problem as context is widely ignored. We present an approach to improve on a generic person detector by

• simplifying the learning problem: local detectors are trained with samples taken from the scene using the generic person detector.
• using temporal context: a robust tracking algorithm is used to propagate the generic person detections.
• using spatial context: the local detectors are jointly trained with multiple views.

Results on the PETS 2009 dataset show significantly improved person detections, especially, during static and dynamic occlusions (e.g., lamp poles and crowded scenes).

the detection results are shown at maximized f-Measure. True positives are shown in green, false positives in red.

If you also like to evaluate your algorithm on the same data, here is the zip file containing:

• the images of both views (frame 1-654: training data, frame 655-844: test data)
• and the annotated ground truth of view001 (format: [frame_number x y width height])