Suspicious Person Retrieval from UAV-sensors based on part level deep features

Abstract

Intelligent video surveillance systems represent a potent tool for preserving human security in public places. Indeed, these surveillance systems are requested in several real-life scenarios, in order to assist security guards by alerting them in abnormal situations and helping them to retrieve a suspicious person. Especially, intelligent video surveillance systems based on UAV-sensors have the asset of monitoring large as well as difficult access spaces. In this scope, we introduce a new approach for suspicious person retrieval from UAV-sensors. The proposed approach implies two complementary phases which are an offline phase and an inference phase. Within these phases, a scene stabilization step is carried out. The offline phase allows building the non-person/person model as well as the person retrieval model. Nonetheless, the inference phase enables to detect persons and retrieve suspicious ones using the already generated models. The main contribution of the proposed approach is the use of part-level deep features in order to retrieve persons. The experimental results validate the contributions of our approach compared to the state-of-the-art approaches.