ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking

• URL: http://arxiv.org/abs/2106.02495v1
• Date: Fri, 4 Jun 2021 14:05:24 GMT
• Title: ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking
• Authors: Bowen Li, Changhong Fu, Fangqiang Ding, Junjie Ye, and Fuling Lin
• Abstract summary: The proposed method integrates an efficient and effective low-light image enhancer into a CF-based tracker. The target-aware mask can be applied to jointly train a target-focused filter that assists the context filter for robust tracking. The results have shown that ADTrack favorably outperforms other state-of-the-art trackers and achieves a real-time speed of 34 frames/s on a single CPU.
• Score: 17.80444543935194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prior correlation filter (CF)-based tracking methods for unmanned aerial vehicles (UAVs) have virtually focused on tracking in the daytime. However, when the night falls, the trackers will encounter more harsh scenes, which can easily lead to tracking failure. In this regard, this work proposes a novel tracker with anti-dark function (ADTrack). The proposed method integrates an efficient and effective low-light image enhancer into a CF-based tracker. Besides, a target-aware mask is simultaneously generated by virtue of image illumination variation. The target-aware mask can be applied to jointly train a target-focused filter that assists the context filter for robust tracking. Specifically, ADTrack adopts dual regression, where the context filter and the target-focused filter restrict each other for dual filter learning. Exhaustive experiments are conducted on typical dark sceneries benchmark, consisting of 37 typical night sequences from authoritative benchmarks, i.e., UAVDark, and our newly constructed benchmark UAVDark70. The results have shown that ADTrack favorably outperforms other state-of-the-art trackers and achieves a real-time speed of 34 frames/s on a single CPU, greatly extending robust UAV tracking to night scenes.

Related papers

• DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs [53.64523622330297]
  Existing nighttime unmanned aerial vehicle (UAV) trackers follow an "Enhance-then-Track" architecture. This separate enhancement and tracking fails to build an end-to-end trainable vision system. We propose Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts.
  arXiv  Detail & Related papers  (2023-09-19T09:59:08Z)
• BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View [56.77287041917277]
  3D Single Object Tracking (SOT) is a fundamental task of computer vision, proving essential for applications like autonomous driving. In this paper, we propose BEVTrack, a simple yet effective baseline method. By estimating the target motion in Bird's-Eye View (BEV) to perform tracking, BEVTrack demonstrates surprising simplicity from various aspects, i.e., network designs, training objectives, and tracking pipeline, while achieving superior performance.
  arXiv  Detail & Related papers  (2023-09-05T12:42:26Z)
• 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)
• Rank-Based Filter Pruning for Real-Time UAV Tracking [11.740436885164833]
  Unmanned aerial vehicle (UAV) tracking has wide potential applications in such as agriculture, navigation, and public security. Discriminative correlation filters (DCF) trackers stand out in the UAV tracking community because of their high efficiency. Model compression is a promising way to narrow the gap between DCF- and deep learning-based trackers.
  arXiv  Detail & Related papers  (2022-07-05T02:13:53Z)
• VariabilityTrack:Multi-Object Tracking with Variable Speed Object Movement [1.6385815610837167]
  Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. We propose a variable speed Kalman filter algorithm based on environmental feedback and improve the matching process.
  arXiv  Detail & Related papers  (2022-03-12T12:39:41Z)
• Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy [63.91005999481061]
  A practical long-term tracker typically contains three key properties, i.e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism. We propose a two-task tracking frame work (named DMTrack) to achieve distractor-aware fast tracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT) philosophy. Our tracker achieves state-of-the-art performance on the LaSOT, OxUvA, TLP, VOT2018LT and VOT 2019LT benchmarks and runs in real-time (3x faster
  arXiv  Detail & Related papers  (2021-04-25T00:59:53Z)
• All-Day Object Tracking for Unmanned Aerial Vehicle [19.10142725484355]
  This work proposes a novel correlation filter based tracker with illumination adaptive and anti dark capability, namely ADTrack. ADTrack exploits image illuminance information to enable adaptability of the model to the given light condition. This work also constructed one UAV nighttime tracking benchmark UAVDark135, comprising of more than 125k manually annotated frames.
  arXiv  Detail & Related papers  (2021-01-21T05:12:37Z)
• DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression [18.044423448896143]
  In this work, we exploit the local maximum points of the response map generated in the detection phase to automatically locate current distractors. Substantial experiments conducted on three challenging UAV benchmarks demonstrate both excellent performance and extraordinary speed.
  arXiv  Detail & Related papers  (2020-08-10T06:08:31Z)
• Efficient Adversarial Attacks for Visual Object Tracking [73.43180372379594]
  We present an end-to-end network FAN (Fast Attack Network) that uses a novel drift loss combined with the embedded feature loss to attack the Siamese network based trackers. Under a single GPU, FAN is efficient in the training speed and has a strong attack performance.
  arXiv  Detail & Related papers  (2020-08-01T08:47:58Z)
• Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination [202.2562153608092]
  We propose a cascaded regression tracker with two sequential stages. In the first stage, we filter out abundant easily-identified negative candidates. In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples.
  arXiv  Detail & Related papers  (2020-06-18T07:48:01Z)
• Keyfilter-Aware Real-Time UAV Object Tracking [28.90501292821063]
  Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) It has two imperfections, i.e., boundary effect and filter corruption. Inspired by simultaneous localization and mapping, key-of-filter is proposed in visual tracking for the first time.
  arXiv  Detail & Related papers  (2020-03-11T11:11:16Z)

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.