Detecting Partially Occluded Objects with an Implicit Shape Model Random Field

Abstract

In this paper, we introduce a formulation for the task of detecting objects based on the information gathered from a standard Implicit Shape Model (ISM). We describe a probabilistic approach in a general random field setting, which enables to effectively detect object instances and additionally identifies all local patches contributing to the different instances. We propose a sparse graph structure and define a semantic label space, specifically tuned to the task of localizing objects. The design of the graph structure then allows to define a novel inference process that efficiently returns a good local minimum of our energy minimization problem. A key benefit of our method is, that we do not have to fix a range for local neighborhood suppression, as necessary for instance in related non maximum suppression approaches. Our inference process implicitly is capable to separate even strongly overlapping object instances. Experimental evaluation compares our method to state-of-the-art in this field on challenging sequences showing competitive and improved results.