Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

• URL: http://arxiv.org/abs/2105.14291v1
• Date: Sat, 29 May 2021 12:59:29 GMT
• Title: Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview
• Authors: Zhaoxin Fan, Yazhi Zhu, Yulin He, Qi Sun, Hongyan Liu and Jun He
• Abstract summary: Object pose detection and tracking has attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Deep learning is the most promising one that has shown better performance than others. This paper presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route.
• Score: 8.442460766094674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, there is lack of survey study about latest development of deep learning based methods. Therefore, this paper presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this paper is limited to methods taking monocular RGB/RGBD data as input, covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods about both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions.

Related papers

• Beyond Traditional Single Object Tracking: A Survey [0.0]
  We visit some of the cutting-edge techniques in vision, such as Sequence Models, Generative Models, Self-supervised Learning, Unsupervised Learning, Reinforcement Learning, Meta-Learning, Continual Learning, and Domain Adaptation. We propose a novel categorization of single object tracking methods based on novel techniques and trends. We analyze the pros and cons of the presented approaches and present a guide for non-traditional techniques in single object tracking.
  arXiv  Detail & Related papers  (2024-05-16T20:55:31Z)
• Deep Learning-Based Object Pose Estimation: A Comprehensive Survey [73.74933379151419]
  We discuss the recent advances in deep learning-based object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks.
  arXiv  Detail & Related papers  (2024-05-13T14:44:22Z)
• Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances [51.70835702029498]
  This review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD.
  arXiv  Detail & Related papers  (2023-09-13T06:48:32Z)
• Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey [10.665235711722076]
  Oriented object detection is one of the most fundamental and challenging tasks in remote sensing. Recent years have witnessed remarkable progress in oriented object detection using deep learning techniques.
  arXiv  Detail & Related papers  (2023-02-21T06:31:53Z)
• A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance [38.88995659233979]
  Small object detection in optical images and videos is a challenging problem. Even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research.
  arXiv  Detail & Related papers  (2022-07-26T14:28:38Z)
• Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking [47.59619420444781]
  Approaches to monocular 3D perception including detection and tracking often yield inferior performance when compared to LiDAR-based techniques. We propose a multi-level fusion method that combines different representations (RGB and pseudo-LiDAR) and temporal information across multiple frames for objects (tracklets) to enhance per-object depth estimation.
  arXiv  Detail & Related papers  (2022-06-08T03:37:59Z)
• Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey [71.10448142010422]
  Multi-object tracking (MOT) aims to associate target objects across video frames in order to obtain entire moving trajectories. Embedding methods play an essential role in object location estimation and temporal identity association in MOT. We first conduct a comprehensive overview with in-depth analysis for embedding methods in MOT from seven different perspectives.
  arXiv  Detail & Related papers  (2022-05-22T06:54:33Z)
• Deep Learning for UAV-based Object Detection and Tracking: A Survey [25.34399619170044]
  Unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS) Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods.
  arXiv  Detail & Related papers  (2021-10-25T04:43:24Z)
• Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective [69.44384540002358]
  We provide a comprehensive and holistic 2D-to-3D perspective to tackle this problem. We categorize the mainstream and milestone approaches since the year 2014 under unified frameworks. We also summarize the pose representation styles, benchmarks, evaluation metrics, and the quantitative performance of popular approaches.
  arXiv  Detail & Related papers  (2021-04-23T11:07:07Z)
• Progressive Object Transfer Detection [84.48927705173494]
  We propose a novel Progressive Object Transfer Detection (POTD) framework. First, POTD can leverage various object supervision of different domains effectively into a progressive detection procedure. Second, POTD consists of two delicate transfer stages, i.e., Low-Shot Transfer Detection (LSTD), and Weakly-Supervised Transfer Detection (WSTD)
  arXiv  Detail & Related papers  (2020-02-12T00:16:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.