Abstract
People search based on semantic attributes presents an important task for several surveillance applications. The aim is to locate a suspect or to find a missing person in public areas based on an appearance description provided by a witness. In this paper, we propose a novel people search method based on an appearance description provided in terms of semantic attributes under uncontrolled acquisition conditions (e.g. gender, worn bags, carried objects, and clothes pattern). To this end, we propose to learn a set of deep semantic attribute classifiers based on the convolutional neural network. Experimental evaluations, based on the confusion matrix and the statistical tests, prove the efficiency of our method compared to state-of-the-art CNN architectures. Further, a comparison with state-of-the-art attribute classification methods is also conducted and confirms the efficiency of our method.
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