A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
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 …
Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection
This study addresses the issue of fusing infrared and visible images that appear differently
for object detection. Aiming at generating an image of high visual quality, previous …
for object detection. Aiming at generating an image of high visual quality, previous …
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 …
Trackformer: Multi-object tracking with transformers
The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about
track initialization, identity, and spatio-temporal trajectories. We formulate this task as a …
track initialization, identity, and spatio-temporal trajectories. We formulate this task as a …
Hota: A higher order metric for evaluating multi-object tracking
Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics
overemphasize the importance of either detection or association. To address this, we …
overemphasize the importance of either detection or association. To address this, we …
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 …
Observation-centric sort: Rethinking sort for robust multi-object tracking
Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that
objects move linearly. While this assumption is acceptable for very short periods of …
objects move linearly. While this assumption is acceptable for very short periods of …
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 …
Track to detect and segment: An online multi-object tracker
Most online multi-object trackers perform object detection stand-alone in a neural net without
any input from tracking. In this paper, we present a new online joint detection and tracking …
any input from tracking. In this paper, we present a new online joint detection and tracking …