Fast Vehicle Identification in Surveillance via Ranked Semantic Sampling Based Embedding

Fast Vehicle Identification in Surveillance via Ranked Semantic Sampling Based Embedding

Feng Zheng, Xin Miao, Heng Huang

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3697-3703. https://doi.org/10.24963/ijcai.2018/514

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.
Keywords:
Machine Learning Applications: Big data ; Scalability
Computer Vision: Computer Vision