Logarithmically improved property regression for crowd counting

Abstract

Crowd counting based on video camera recordings faces two major problems, namely inter-occlusion among the people, and perspective scaling. Though the former issue has been adequately addressed using different regression and model-based schemes, a solution to the later problem remains an open problem so far. This paper proposes a novel scene-independent solution to perspective scaling. We show that it supports promising results. A property matrix, combining both a grey-level co-occurrence matrix and segmentation properties, is first obtained which is subsequently weighted using logarithmic relationships between pixel distances and foreground regions. We apply a Gaussian process regression, using a compounded kernel, to acquire an estimate for the crowd count. We show that results are comparable to those obtained when using more complex and costly techniques.