Camouflaged Object Detection and Tracking: A Survey

• URL: http://arxiv.org/abs/2012.13581v1
• Date: Fri, 25 Dec 2020 14:15:45 GMT
• Title: Camouflaged Object Detection and Tracking: A Survey
• Authors: Ajoy Mondal
• Abstract summary: We review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This article also addresses several issues of interest as well as future research direction on this area.
• Score: 7.056222499095849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and several essential survey papers exist. However, the research on camouflage object detection and tracking limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this article, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This article also addresses several issues of interest as well as future research direction on this area. We hope this review will help the reader to learn the recent advances in camouflaged object detection and tracking.

Related papers

• VOVTrack: Exploring the Potentiality in Videos for Open-Vocabulary Object Tracking [61.56592503861093]
  This issue amalgamates the complexities of open-vocabulary object detection (OVD) and multi-object tracking (MOT) Existing approaches to OVMOT often merge OVD and MOT methodologies as separate modules, predominantly focusing on the problem through an image-centric lens. We propose VOVTrack, a novel method that integrates object states relevant to MOT and video-centric training to address this challenge from a video object tracking standpoint.
  arXiv  Detail & Related papers  (2024-10-11T05:01:49Z)
• Camouflaged_Object_Tracking__A_Benchmark [13.001689702214573]
  We introduce the Camouflaged Object Tracking dataset (COTD), a benchmark for evaluating camouflaged object tracking methods. COTD comprises 200 sequences and approximately 80,000 frames, each annotated with detailed bounding boxes. Our evaluation of 20 existing tracking algorithms reveals significant deficiencies in their performance with camouflaged objects. We propose a novel tracking framework, HiPTrack-MLS, which demonstrates promising results in improving tracking performance for camouflaged objects.
  arXiv  Detail & Related papers  (2024-08-25T15:56:33Z)
• V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results [142.5704093410454]
  The V3Det Challenge 2024 aims to push the boundaries of object detection research. The challenge consists of two tracks: Vast Vocabulary Object Detection and Open Vocabulary Object Detection. We aim to inspire future research directions in vast vocabulary and open-vocabulary object detection.
  arXiv  Detail & Related papers  (2024-06-17T16:58:51Z)
• 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)
• Single Object Tracking Research: A Survey [44.24280758718638]
  This paper presents the rationale and works of two most popular tracking frameworks in past ten years. We present some deep learning based tracking methods categorized by different network structures. We also introduce some classical strategies for handling the challenges in tracking problem.
  arXiv  Detail & Related papers  (2022-04-25T02:59:15Z)
• Detection, Recognition, and Tracking: A Survey [0.0]
  In Computer Vision and Multimedia, it is increasingly important to detect, recognize and track objects in images and/or videos. Many applications, such as facial recognition, surveillance, animation, are used for tracking features and/or people. This literature review focuses on some novel techniques on object detection and recognition, and how to apply tracking algorithms to the detected features to track the objects' movements.
  arXiv  Detail & Related papers  (2022-03-22T17:11:24Z)
• Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview [8.442460766094674]
  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.
  arXiv  Detail & Related papers  (2021-05-29T12:59:29Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)
• Track to Detect and Segment: An Online Multi-Object Tracker [81.15608245513208]
  TraDeS is an online joint detection and tracking model, exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking offset by a cost volume, which is used to propagate previous object features.
  arXiv  Detail & Related papers  (2021-03-16T02:34:06Z)
• Detecting Invisible People [58.49425715635312]
  We re-purpose tracking benchmarks and propose new metrics for the task of detecting invisible objects. We demonstrate that current detection and tracking systems perform dramatically worse on this task. Second, we build dynamic models that explicitly reason in 3D, making use of observations produced by state-of-the-art monocular depth estimation networks.
  arXiv  Detail & Related papers  (2020-12-15T16:54:45Z)

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.