关键词: 物体计数, 细胞计数, 密度估计

Abstract:  Object counting gains an important role in many fields, such as estimating the number of cells in a microscopic image or predicting the number of pedestrians in surveillance video frames. Many researches have been done to accurately estimate the count, among which the density estimation framework first estimates the density image, then integrates probability over the whole density image to get the counting number. Under this framework, this paper proposes an algorithm based on minimizing square error to infer the density image. This algorithm has an analytical solution, and can reduce training time in a computationally efficient and stable manner. The prediction result has a competitive error rate to other density estimation algorithms. Combined with the technique of neighbor feature smoothing, the estimated density image is very similar to ground truth density in human vision.

Key words: object counting, cells counting, density estimation