Matching heads of multiple people in multiple camera networks

In this paper, we aim to find the matches of head detections from different cameras. Matching of heads detected in different cameras and assigning the same label to the detection results which are actually from the same person can help to track the person robustly in severe occlusion because matching process can give more clues when the tracked person is even totally occluded in one camera. We present two similarity measurement methods for head matching based on geometry information: homography similarity and epipolar similarity. Each similarity is based on homography and fundamental matrix respectively. Experimental results show that our similarity measures have validity to use for matching head detections. We also compare two similarities and analyze what strong point of each method is.