Visual Object Tracking on Multi-modal RGB-D Videos: A Review

• URL: http://arxiv.org/abs/2201.09207v3
• Date: Sun, 17 Mar 2024 05:10:23 GMT
• Title: Visual Object Tracking on Multi-modal RGB-D Videos: A Review
• Authors: Xue-Feng Zhu, Tianyang Xu, Xiao-Jun Wu,
• Abstract summary: The goal of this review is to summarize the relative knowledge of the research filed of RGB-D tracking. To be specific, we will generalize the related RGB-D tracking benchmarking datasets as well as the corresponding performance measurements.
• Score: 16.098468526632473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of visual object tracking has continued for decades. Recent years, as the wide accessibility of the low-cost RGBD sensors, the task of visual object tracking on RGB-D videos has drawn much attention. Compared to conventional RGB-only tracking, the RGB-D videos can provide more information that facilitates objecting tracking in some complicated scenarios. The goal of this review is to summarize the relative knowledge of the research filed of RGB-D tracking. To be specific, we will generalize the related RGB-D tracking benchmarking datasets as well as the corresponding performance measurements. Besides, the existing RGB-D tracking methods are summarized in the paper. Moreover, we discuss the possible future direction in the field of RGB-D tracking.

Related papers

• ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection [51.16181295385818]
  We first collect an annotated RGB-D video SODOD (DSOD-100) dataset, which contains 100 videos within a total of 9,362 frames. All the frames in each video are manually annotated to a high-quality saliency annotation. We propose a new baseline model, named attentive triple-fusion network (ATF-Net) for RGB-D salient object detection.
  arXiv  Detail & Related papers  (2024-06-18T12:09:43Z)
• ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data [75.73063721067608]
  We propose a new RGB-D tracking dataset for both static and dynamic scenes captured by consumer-grade LiDAR scanners equipped on Apple's iPhone and iPad. ARKitTrack contains 300 RGB-D sequences, 455 targets, and 229.7K video frames in total. In-depth empirical analysis has verified that the ARKitTrack dataset can significantly facilitate RGB-D tracking and that the proposed baseline method compares favorably against the state of the arts.
  arXiv  Detail & Related papers  (2023-03-24T09:51:13Z)
• Learning Dual-Fused Modality-Aware Representations for RGBD Tracking [67.14537242378988]
  Compared with the traditional RGB object tracking, the addition of the depth modality can effectively solve the target and background interference. Some existing RGBD trackers use the two modalities separately and thus some particularly useful shared information between them is ignored. We propose a novel Dual-fused Modality-aware Tracker (termed DMTracker) which aims to learn informative and discriminative representations of the target objects for robust RGBD tracking.
  arXiv  Detail & Related papers  (2022-11-06T07:59:07Z)
• RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking [30.448658049744775]
  Given a limited amount of annotated RGB-D tracking data, most state-of-the-art RGB-D trackers are simple extensions of high-performance RGB-only trackers. To address the dataset deficiency issue, a new RGB-D dataset named RGBD1K is released in this paper.
  arXiv  Detail & Related papers  (2022-08-21T03:07:36Z)
• RGBD Object Tracking: An In-depth Review [89.96221353160831]
  We firstly review RGBD object trackers from different perspectives, including RGBD fusion, depth usage, and tracking framework. We benchmark a representative set of RGBD trackers, and give detailed analyses based on their performances.
  arXiv  Detail & Related papers  (2022-03-26T18:53:51Z)
• Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images [89.81919625224103]
  Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. We present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB-D saliency detection.
  arXiv  Detail & Related papers  (2022-01-01T03:02:27Z)
• DepthTrack : Unveiling the Power of RGBD Tracking [29.457114656913944]
  This work introduces a new RGBD tracking dataset - Depth-Track. It has twice as many sequences (200) and scene types (40) than in the largest existing dataset. The average length of the sequences (1473), the number of deformable objects (16) and the number of tracking attributes (15) have been increased.
  arXiv  Detail & Related papers  (2021-08-31T16:42:38Z)
• Synergistic saliency and depth prediction for RGB-D saliency detection [76.27406945671379]
  Existing RGB-D saliency datasets are small, which may lead to overfitting and limited generalization for diverse scenarios. We propose a semi-supervised system for RGB-D saliency detection that can be trained on smaller RGB-D saliency datasets without saliency ground truth.
  arXiv  Detail & Related papers  (2020-07-03T14:24:41Z)

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.