A Multimodal Deep Learning Framework for Robust Person Re-Identification

Abstract

Identifying-matching the same individuals has become a complex thing in strengthening security and it shows seamless living within smart cities. This task involves detecting and matching a person across multiple camera views for surveillance and safety applications. The capability and ability are able to accurately recognize individuals from diversified visual data sources. The new pathways of computer vision accelerated the research progress with deep neural networks to process high quality surveillance data. By analyzing the structural elements of a Re-ID framework, one can differentiate between closed-world and open-world identification scenarios. This work proceeds shows data preprocessing strategies for person re-identification and evaluates deep learning approaches using widely recognized benchmark datasets. Finally, it mainly focus on the vital key role on developing an effective deep learning and that leverages various multimodal feature extraction aimed at performance analysis identification and performance accuracy. We proposed new technique evaluated to performance on human recognition capabilities.