Detectors

Now that the filtergraph is operating, it's time to start implementing some detector nodes. Here are some "sourcing links" I have gathered as inspiration and potential starting points (for my own reference).

• VLFeat, efficient implementation of several feature detection algorithms including SIFT
• SIFTGPU - SIFT implementation on GPU
• TLD Tracking, Learning, Detection - adaptive object detection and tracking
• OpenCV tutorial on face tracking
• Another OpenCV tutorial on face tracking
• Yet another OpenCV tutoorial on face tracking courtesy of Wilow Garage
• SceneLib Monocular SLAM implementation
• ScaViSLAM Monocular SLAM implementation
• OpenCL SURF Implementation
• Another OpenCL SURF Implementation
• OpenCL tutorial for Linux
• Training free real-tim feature tracker
• Saliency detection algorithm

Performance will be an issue with many concurrently active detectors, and I have been giving that some thought as how to solve that. One optimization strategy would be to lower the number of invocations of each detector to the minimum. Another is adaptively disable detectors. For example face detection may run every 100 frames (4 seconds) until a face is detected, upon which it may be run more often as long as faces are present.

Another optimization strategy is to use a saliency (areas of interest) detection algorithm and increase rate of the other detectors in the areas with high interest.

I think the approach now is just to get a simple detector up and working, and take it from there.