Deep learning for person re-identification: A survey and outlook
Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-
overlapping cameras. With the advancement of deep neural networks and increasing …
overlapping cameras. With the advancement of deep neural networks and increasing …
Deep learning-based methods for person re-identification: A comprehensive review
D Wu, SJ Zheng, XP Zhang, CA Yuan, F Cheng… - Neurocomputing, 2019 - Elsevier
In recent years, person re-identification (ReID) has received much attention since it is a
fundamental task in intelligent surveillance systems and has widespread application …
fundamental task in intelligent surveillance systems and has widespread application …
Strongsort: Make deepsort great again
Y Du, Z Zhao, Y Song, Y Zhao, F Su… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly,
remarkable progresses have been achieved. However, the existing methods tend to use …
remarkable progresses have been achieved. However, the existing methods tend to use …
Bytetrack: Multi-object tracking by associating every detection box
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …
videos. Most methods obtain identities by associating detection boxes whose scores are …
Transtrack: Multiple object tracking with transformer
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple
object tracking problems. TransTrack leverages the transformer architecture, which is an …
object tracking problems. TransTrack leverages the transformer architecture, which is an …
Nformer: Robust person re-identification with neighbor transformer
Person re-identification aims to retrieve persons in highly varying settings across different
cameras and scenarios, in which robust and discriminative representation learning is …
cameras and scenarios, in which robust and discriminative representation learning is …
Fairmot: On the fairness of detection and re-identification in multiple object tracking
Multi-object tracking (MOT) is an important problem in computer vision which has a wide
range of applications. Formulating MOT as multi-task learning of object detection and re-ID …
range of applications. Formulating MOT as multi-task learning of object detection and re-ID …
Beyond appearance: a semantic controllable self-supervised learning framework for human-centric visual tasks
Human-centric visual tasks have attracted increasing research attention due to their
widespread applications. In this paper, we aim to learn a general human representation from …
widespread applications. In this paper, we aim to learn a general human representation from …
Towards real-time multi-object tracking
Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection
paradigm. It has 1) a detection model for target localization and 2) an appearance …
paradigm. It has 1) a detection model for target localization and 2) an appearance …
Rethinking the competition between detection and reid in multiobject tracking
Due to balanced accuracy and speed, one-shot models which jointly learn detection and
identification embeddings, have drawn great attention in multi-object tracking (MOT) …
identification embeddings, have drawn great attention in multi-object tracking (MOT) …