Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement

• URL: http://arxiv.org/abs/2308.15816v2
• Date: Thu, 31 Aug 2023 08:14:17 GMT
• Title: Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement
• Authors: Basit Alawode, Fayaz Ali Dharejo, Mehnaz Ummar, Yuhang Guo, Arif Mahmood, Naoufel Werghi, Fahad Shahbaz Khan, Jiri Matas, Sajid Javed
• Abstract summary: This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT) It poses distinct challenges; the underwater environment exhibits non-uniform lighting conditions, low visibility, lack of sharpness, low contrast, camouflage, and reflections from suspended particles. We propose a novel underwater image enhancement algorithm designed specifically to boost tracking quality. The method has resulted in a significant performance improvement, of up to 5.0% AUC, of state-of-the-art (SOTA) visual trackers.
• Score: 70.2429155741593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT). Despite its significance, underwater tracking has remained unexplored due to data inaccessibility. It poses distinct challenges; the underwater environment exhibits non-uniform lighting conditions, low visibility, lack of sharpness, low contrast, camouflage, and reflections from suspended particles. Performance of traditional tracking methods designed primarily for terrestrial or open-air scenarios drops in such conditions. We address the problem by proposing a novel underwater image enhancement algorithm designed specifically to boost tracking quality. The method has resulted in a significant performance improvement, of up to 5.0% AUC, of state-of-the-art (SOTA) visual trackers. To develop robust and accurate UVOT methods, large-scale datasets are required. To this end, we introduce a large-scale UVOT benchmark dataset consisting of 400 video segments and 275,000 manually annotated frames enabling underwater training and evaluation of deep trackers. The videos are labelled with several underwater-specific tracking attributes including watercolor variation, target distractors, camouflage, target relative size, and low visibility conditions. The UVOT400 dataset, tracking results, and the code are publicly available on: https://github.com/BasitAlawode/UWVOT400.

Related papers

• Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions [48.529493393948435]
  The visible-light camera has emerged as an essential imaging sensor for marine surface vessels in intelligent waterborne transportation systems. The visual imaging quality inevitably suffers from several kinds of degradations under complex weather conditions. We develop a general-purpose multi-scene visibility enhancement method to restore degraded images captured under different weather conditions.
  arXiv  Detail & Related papers  (2024-09-02T23:46:27Z)
• Low-Light Object Tracking: A Benchmark [9.798869093713067]
  We introduce LLOT, a benchmark specifically designed for Low-Light Object Tracking. LLOT comprises 269 challenging sequences with a total of over 132K frames, each carefully annotated with bounding boxes. In response, we propose H-DCPT, a novel tracker that incorporates historical and darkness clue prompts to set a stronger baseline.
  arXiv  Detail & Related papers  (2024-08-21T09:27:57Z)
• Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion [30.122666238416716]
  We propose a novel pipeline for generating underwater images using accurate terrestrial depth data. This approach facilitates the training of supervised models for underwater depth estimation. We introduce a unique Depth2Underwater ControlNet, trained on specially prepared Underwater, Depth, Text data triplets.
  arXiv  Detail & Related papers  (2023-12-19T08:56:33Z)
• Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
  This paper seeks transferable prior knowledge from detector-friendly images. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps. Our method with higher speeds and less parameters still performs better than transformer-based detectors.
  arXiv  Detail & Related papers  (2023-08-24T12:32:46Z)
• Underwater Object Tracker: UOSTrack for Marine Organism Grasping of Underwater Vehicles [7.494346355005127]
  This paper proposes Underwater OSTrack, which consists of underwater image and open-air sequence hybrid training (UOHT) and motion-based post-processing (MBPP) UOSTrack achieves an average performance improvement of 4.41% and 7.98% maximum compared to state-of-the-art methods on various benchmarks.
  arXiv  Detail & Related papers  (2023-01-04T08:22:34Z)
• AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility [125.77396380698639]
  AVisT is a benchmark for visual tracking in diverse scenarios with adverse visibility. AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios. We benchmark 17 popular and recent trackers on AVisT with detailed analysis of their tracking performance across attributes.
  arXiv  Detail & Related papers  (2022-08-14T17:49:37Z)
• Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline [80.13652104204691]
  In this paper, we construct a large-scale benchmark with high diversity for visible-thermal UAV tracking (VTUAV) We provide a coarse-to-fine attribute annotation, where frame-level attributes are provided to exploit the potential of challenge-specific trackers. In addition, we design a new RGB-T baseline, named Hierarchical Multi-modal Fusion Tracker (HMFT), which fuses RGB-T data in various levels.
  arXiv  Detail & Related papers  (2022-04-08T15:22:33Z)
• Benchmarking Deep Trackers on Aerial Videos [5.414308305392762]
  In this paper, we compare ten trackers based on deep learning techniques on four aerial datasets. We choose top performing trackers utilizing different approaches, specifically tracking by detection, discriminative correlation filters, Siamese networks and reinforcement learning. Our findings indicate that the trackers perform significantly worse in aerial datasets compared to standard ground level videos.
  arXiv  Detail & Related papers  (2021-03-24T01:45:19Z)
• Learning Monocular Dense Depth from Events [53.078665310545745]
  Event cameras produce brightness changes in the form of a stream of asynchronous events instead of intensity frames. Recent learning-based approaches have been applied to event-based data, such as monocular depth prediction. We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods.
  arXiv  Detail & Related papers  (2020-10-16T12:36:23Z)

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.