Real-time multiple object instances detection

Chengli Xie, Jinqiao Wang, Yifan Zhang, Hanqing Lu

Abstract: In this paper, we present a novel, real-time multiple object instance detection system via template matching and pairwise classification. Instance detection aims to find and locate exactly the same object instances as specified. Our system is composed of two heterogeneous stages. The first stage adopts instance-specific detection to generate candidates. And the second stage makes use of a pairwise-based classifier across instance categories to test and verify these candidates with respect to templates. Experiments show the superiority of our approach.