I want to automatically track heads in CCTV records.
Sometimes I need to re-identify heads because of multiple heads crossing each other. So I continually need to extract features from tracked heads so that I will be able to re-identify them when needed. Those tracked heads are seen from any angle.
What would be the best features to describe heads?
EDIT: Precisions
- I work with multiple cameras (15) in differents positions generally placed from 0.5 to 20 meters to the people. Those cameras are inside a building, the luminosity is quite good (but I also would like to work with IR during the night, but for now working with light), cameras are 2048x1536 @ 20fps with color.
- Cameras are barely overlapping
- For now, I use https://github.com/Russell91/TensorBox to detect heads accurately from any angle and it works very well (I have almost the same angle and exposition than in TensorBox readme image).
- As TensorBox is very slow (20ms->70ms/image on gpu) I use it only when there is motion and only 2times/sec max. I use optical flow (calcOpticalFlowPyrLK from openCV) to follow heads between two head detections.
- I don't have any information on people. When there is someone new, I create a new identity. Then I try to follow him on the camera network and save the path he followed.
- I am not able to use face detectors for re-identification as cameras do not necessarily see them from the front. But this could improve re-identification.
- So there are two different cases to handle with re-identification:
- Multiple persons overlap
- Person X is moving to another camera
I can easily track persons' heads for now, but I cannot identify them easily because I cannot distinguish them. I think I will need a simple multi-class classifier, but I don't know what would be the best features to describe persons' heads that are moving in any directions.
Thanks for your help!
