Year: 2018
Type: conference-paper
Abstract: Identifying vehicles across cameras in traffic surveillance is fundamentally important for public safety purposes. However, despite some preliminary work, the rapid vehicle search in large-scale datasets has not been investigated. Moreover, modelling a view-invariant similarity between vehicle images from different views is still highly challenging. To address the problems, in this paper, we propose a Ranked Semantic Sampling (RSS) guided binary embedding method for fast cross-view vehicle Re-IDentification (Re-ID). The search can be conducted by efficiently computing similarities in the projected space. Unlike previous methods using random sampling, we design tree-structured attributes to guide the mini-batch sampling. The ranked pairs of hard samples in the mini-batch can improve the convergence of optimization. By minimizing a novel ranked semantic distance loss defined according to the structure, the learned Hamming distance is view-invariant, which enables cross-view Re-ID. The experimental results demonstrate that RSS outperforms the state-of-the-art approaches and the learned embedding from one dataset can be transferred to achieve the task of vehicle Re-ID on another dataset.
Authors: Feng Zheng, Xin Miao, Heng Huang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
Awards: ABI Innovation: A New Automated Data Integration, Annotations, and Interaction Network Inference System for Analyzing Drosophila Gene Expression, III: Medium: Collaborative Research: Robust Large-Scale Electronic Medical Record Data Mining Framework to Conduct Risk Stratification for Personalized Intervention, SCH: EXP: Collaborative Research: Privacy-Preserving Framework for Publishing Electronic Healthcare Records
OpenAlex ID: W2807859372