GSM: Graph Similarity Model for Multi-Object Tracking

By Qiankun Liu, Qi Chu, Bin Liu, Nenghai Yu. 2020

128 citations recorded by the source corpus.

The popular tracking-by-detection paradigm for multi-object tracking (MOT) focuses on solving data association problem, of which a robust similarity model lies in the heart. Most previous works make effort to improve feature representation for individual object while leaving the relations among objects less explored, which may be problematic in some complex scenarios. In this paper, we focus on leveraging the relations among objects to improve robustness of the similarity model. To this end, we propose a novel graph representation that takes both the feature of individual object and the relations among objects into consideration. Besides, a graph matching module is specially designed for the proposed graph representation to alleviate the impact of unreliable relations. With the help of the graph representation and the graph matching module, the proposed graph similarity model, named GSM, is more robust to the occlusion and the targets sharing similar appearance. We conduct extensive experiments on challenging MOT benchmarks and the experimental results demonstrate the effectiveness of the proposed method.

Subfields: Computer Vision and Pattern Recognition, Graph Learning, Signal Processing

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Citations in the corpus

Occluded Video Instance Segmentation: A Benchmark (2021, background): “[84,52] exploit the topology between objects to track the occluded objects.”