Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification

04/30/2021
by   Yichao Yan, et al.
0

Video-based person re-identification (re-ID) is an important research topic in computer vision. The key to tackling the challenging task is to exploit both spatial and temporal clues in video sequences. In this work, we propose a novel graph-based framework, namely Multi-Granular Hypergraph (MGH), to pursue better representational capabilities by modeling spatiotemporal dependencies in terms of multiple granularities. Specifically, hypergraphs with different spatial granularities are constructed using various levels of part-based features across the video sequence. In each hypergraph, different temporal granularities are captured by hyperedges that connect a set of graph nodes (i.e., part-based features) across different temporal ranges. Two critical issues (misalignment and occlusion) are explicitly addressed by the proposed hypergraph propagation and feature aggregation schemes. Finally, we further enhance the overall video representation by learning more diversified graph-level representations of multiple granularities based on mutual information minimization. Extensive experiments on three widely adopted benchmarks clearly demonstrate the effectiveness of the proposed framework. Notably, 90.0 is achieved using MGH, outperforming the state-of-the-arts. Code is available at https://github.com/daodaofr/hypergraph_reid.

READ FULL TEXT

page 3

page 8

research
04/15/2021

Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos

Video-based person re-identification aims to match pedestrians from vide...
research
04/29/2021

Learning Multi-Attention Context Graph for Group-Based Re-Identification

Learning to re-identify or retrieve a group of people across non-overlap...
research
11/09/2018

STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification

In this work, we propose a novel Spatial-Temporal Attention (STA) approa...
research
10/12/2021

MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification

As a challenging task, unsupervised person ReID aims to match the same i...
research
07/16/2020

Appearance-Preserving 3D Convolution for Video-based Person Re-identification

Due to the imperfect person detection results and posture changes, tempo...
research
09/05/2019

Adaptive Graph Representation Learning for Video Person Re-identification

Recent years have witnessed a great development of deep learning based v...
research
10/17/2019

Video Person Re-Identification using Learned Clip Similarity Aggregation

We address the challenging task of video-based person re-identification....

Please sign up or login with your details

Forgot password? Click here to reset