Video-Based Person Re-Identification
36 papers with code • 0 benchmarks • 6 datasets
Video-based person re-identification (reID) aims to retrieve person videos with the same identity as a query person across multiple cameras
Benchmarks
These leaderboards are used to track progress in Video-Based Person Re-Identification
MARS
DukeMTMC-VideoReID
iLIDS-VID
Airport
P-DESTRE
Latest papers with no code
Attention-based Shape and Gait Representations Learning for Video-based Cloth-Changing Person Re-Identification
Our ASGL framework improves Re-ID performance under clothing variations by learning clothing-invariant gait cues using a Spatial-Temporal Graph Attention Network (ST-GAT).
From Synthetic to Real: Unveiling the Power of Synthetic Data for Video Person Re-ID
In this paper, we study a new problem of cross-domain video based person re-identification (Re-ID).
Video-based Person Re-identification with Long Short-Term Representation Learning
Meanwhile, to extract short-term representations, we propose a Bi-direction Motion Estimator (BME), in which reciprocal motion information is efficiently extracted from consecutive frames.
TVPR: Text-to-Video Person Retrieval and a New Benchmark
To the best of our knowledge, TVPRN is the first successful attempt to use video for text-based person retrieval task and has achieved state-of-the-art performance on TVPReid dataset.
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification
In this work, the changes of views, posture, background and modal discrepancy are considered as the main factors that cause the perturbations of person identity features.
Deep Learning for Video-based Person Re-Identification: A Survey
In the last couple of years, deep learning on video re-ID has continuously achieved surprising results on public datasets, with various approaches being developed to handle diverse problems in video re-ID.
Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification
We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages.
Event-Guided Person Re-Identification via Sparse-Dense Complementary Learning
Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications.
Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification
In video person re-identification (Re-ID), the network must consistently extract features of the target person from successive frames.
Multi-Granularity Graph Pooling for Video-based Person Re-Identification
To downsample the graph, we propose a multi-head full attention graph pooling (MHFAPool) layer, which integrates the advantages of existing node clustering and node selection pooling methods.
