Fast Multi-Aspect 2D Human Detection

Abstract

We address the problem of detecting human figures in images, taking into account that the image of the human figure may be taken from a range of viewpoints. We capture the geometric deformations of the 2D human figure using an extension of the Common Factor Model (CFM) of Lan and Huttenlocher. The key contribution of the paper is an improved iterative message passing inference algorithm that runs faster than the original CFM algorithm. This is based on the insight that messages created using the distance transform are shift invariant and therefore messages can be created once and then shifted for subsequent iterations. Since shifting ( O (1) complexity) is faster than computing a distance transform ( O ( n ) complexity), a significant speedup is observed in the experiments. We demonstrate the effectiveness of the new model for the human parsing problem using the Iterative Parsing data set and results are competitive with the state of the art detection algorithm of Andriluka, et al.