Pedestrian Detection Aided by Deep Learning Attributes Task
Deep Learning methods have achieved great successes in pedestrian detection owing to their ability of learning discriminative features from pixel level. However, most of the popular methods only consider using the deep structure as a single feature extractor (one attribute) which may confuse positive with hard negative samples. To address this ambiguity, this work jointly learns three different attributes, including parts, deformation and similarity attributes. This paper proposes a new deep network which jointly optimizes the three attributes and formulates them to form a binary classification task. Extensive experiments show that the proposed method outperforms competing methods on the challenging Caltech and ETH benchmarks.
CCPR