Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology

Abstract

Person Re-Identification (Re-ID) is a very important task in video surveillance systems such as tracking people, finding people in public places, or analysing customer behavior in supermarkets. Although there have been many works to solve this problem, there are still remaining challenges such as large-scale datasets, imbalanced data, viewpoint, finegrained data (attributes), the Local Features are not employed at semantic level in online stage of Re-ID task, furthermore, the imbalanced data problem of attributes are not taken into consideration. This paper has proposed a Unified Re-ID system consisted of three main modules such as Pedestrian Attribute Ontology (PAO), Local Multi-task DCNN (Local MDCNN), Imbalance Data Solver (IDS). The new main point of our Re-ID system is the power of mutual support of PAO, Local MDCNN and IDS to exploit the inner-group correlations of attributes and pre-filter the mismatch candidates from Gallery set based on semantic information as Fashion Attributes and Facial Attributes, to solve the imbalanced data of attributes without adjusting network architecture and data augmentation. We experimented on the well-known Market1501 dataset. The experimental results have shown the effectiveness of our Re-ID system and it could achieve the higher performance on Market1501 dataset in comparison to some state-of-the-art Re-ID methods.

Ontological Model for Contextual Data Defining Time Series for Emotion Recognition and Analysis

“…Pedestrian Attributes Ontology (PAO) [56] is one of three main modules of the Unified Re-ID (re-identification) system. It allows a person's appearance to be described with details such as kind of clothes and shoes as well as wearing a hat or glasses.…”

An Overview of Facial Attribute Learning

“…Finally, using ontology in AL to support semantic of images is also common such as in a study by Nguyen et al (2018), Ly et al (2019), Ly et al (2020).…”