Deep reinforcement learning in computer vision: a comprehensive survey
Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …
the powerful representation of deep neural networks. Recent works have demonstrated the …
Deep learning in multi-object detection and tracking: state of the art
Object detection and tracking is one of the most important and challenging branches in
computer vision, and have been widely applied in various fields, such as health-care …
computer vision, and have been widely applied in various fields, such as health-care …
Pointodyssey: A large-scale synthetic dataset for long-term point tracking
We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework,
for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to …
for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to …
Motr: End-to-end multiple-object tracking with transformer
Temporal modeling of objects is a key challenge in multiple-object tracking (MOT). Existing
methods track by associating detections through motion-based and appearance-based …
methods track by associating detections through motion-based and appearance-based …
Motrv2: Bootstrap** end-to-end multi-object tracking by pretrained object detectors
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end
multi-object tracking with a pretrained object detector. Existing end-to-end methods, eg …
multi-object tracking with a pretrained object detector. Existing end-to-end methods, eg …
Dancetrack: Multi-object tracking in uniform appearance and diverse motion
A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization,
and following re-identification (re-ID) for object association. This pipeline is partially …
and following re-identification (re-ID) for object association. This pipeline is partially …
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 …
Generalized intersection over union: A metric and a loss for bounding box regression
Abstract Intersection over Union (IoU) is the most popular evaluation metric used in the
object detection benchmarks. However, there is a gap between optimizing the commonly …
object detection benchmarks. However, there is a gap between optimizing the commonly …
Transmot: Spatial-temporal graph transformer for multiple object tracking
Tracking multiple objects in videos relies on modeling the spatial-temporal interactions of
the objects. In this paper, we propose TransMOT, which leverages powerful graph …
the objects. In this paper, we propose TransMOT, which leverages powerful graph …