Recent advances of monocular 2d and 3d human pose estimation: A deep learning perspective
Estimation of the human pose from a monocular camera has been an emerging research
topic in the computer vision community with many applications. Recently, benefiting from the …
topic in the computer vision community with many applications. Recently, benefiting from the …
Deep learning on monocular object pose detection and tracking: A comprehensive overview
Object pose detection and tracking has recently attracted increasing attention due to its wide
applications in many areas, such as autonomous driving, robotics, and augmented reality …
applications in many areas, such as autonomous driving, robotics, and augmented reality …
Voxelnext: Fully sparse voxelnet for 3d object detection and tracking
Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers,
and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …
and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …
Universal instance perception as object discovery and retrieval
All instance perception tasks aim at finding certain objects specified by some queries such
as category names, language expressions, and target annotations, but this complete field …
as category names, language expressions, and target annotations, but this complete field …
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 …
BoT-SORT: Robust associations multi-pedestrian tracking
N Aharon, R Orfaig, BZ Bobrovsky - arXiv preprint arXiv:2206.14651, 2022 - arxiv.org
The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene,
while keeping a unique identifier for each object. In this paper, we present a new robust …
while keeping a unique identifier for each object. In this paper, we present a new robust …
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 …
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 …
Towards grand unification of object tracking
We present a unified method, termed Unicorn, that can simultaneously solve four tracking
problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters …
problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters …
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 …