CoDeL: A Human Co-Detection and Labeling Framework

Abstract

We propose a co-detection and labeling (CoDeL) framework to identify persons that contain self-consistent appearance in multiple images. Our CoDeL model builds upon the deformable part-based model to detect human hypotheses and exploits cross-image correspondence via a matching classifier. Relying on a Gaussian process, this matching classifier models the similarity of two hypotheses and efficiently captures the relative importance contributed by various visual features, reducing the adverse effect of scattered occlusion. Further, the detector and matching classifier together make our model fit into a semi-supervised co-training framework, which can get enhanced results with a small amount of labeled training data. Our CoDeL model achieves decent performance on existing and new benchmark datasets.