Person search over security video surveillance systems using deep learning methods: A review

Abstract

Person search has become one of the most critical and challenging applications in today's video surveillance systems. It helps in locating a person in surveillance videos, which is plausible only with advanced deep learning models, large scale datasets and high compute power GPUs. This survey features exhaustive analysis of deep learning based person search through image, textual and attributes based description. The image based person search is reviewed based on aspects such as region proposal consideration, feature representation, context information and compute complexity. The text based person search is reviewed based on the aspects of feature representation and alignment. The attribute based person search is reviewed based on the aspect of high level and low level feature representation. The paper summarizes more than 100 research works and provides future perspectives for enhancements with the objective of guiding and facilitating the development of better solutions in future. We believe that this exclusive review on deep learning based person search for video surveillance security systems will facilitate better systematization.