Unsupervised Domain Adaptation for Nighttime Aerial Tracking

• URL: http://arxiv.org/abs/2203.10541v1
• Date: Sun, 20 Mar 2022 12:35:09 GMT
• Title: Unsupervised Domain Adaptation for Nighttime Aerial Tracking
• Authors: Junjie Ye, Changhong Fu, Guangze Zheng, Danda Pani Paudel, Guang Chen
• Abstract summary: This work develops a novel unsupervised domain adaptation framework for nighttime aerial tracking. A unique object discovery approach is provided to generate training patches from raw nighttime tracking videos. With a Transformer day/night feature discriminator, the daytime tracking model is adversarially trained to track at night.
• Score: 27.595253191781904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications. This work instead develops a novel unsupervised domain adaptation framework for nighttime aerial tracking (named UDAT). Specifically, a unique object discovery approach is provided to generate training patches from raw nighttime tracking videos. To tackle the domain discrepancy, we employ a Transformer-based bridging layer post to the feature extractor to align image features from both domains. With a Transformer day/night feature discriminator, the daytime tracking model is adversarially trained to track at night. Moreover, we construct a pioneering benchmark namely NAT2021 for unsupervised domain adaptive nighttime tracking, which comprises a test set of 180 manually annotated tracking sequences and a train set of over 276k unlabelled nighttime tracking frames. Exhaustive experiments demonstrate the robustness and domain adaptability of the proposed framework in nighttime aerial tracking. The code and benchmark are available at https://github.com/vision4robotics/UDAT.

Related papers

• DaDiff: Domain-aware Diffusion Model for Nighttime UAV Tracking [21.255323355862057]
  This work proposes a novel progressive alignment paradigm, named domain-aware diffusion model (DaDiff) DaDiff aligning nighttime LR object features to the daytime by virtue of progressive and stable generations. An elaborate nighttime UAV tracking benchmark is constructed for LR objects, consisting of 100 annotated sequences.
  arXiv  Detail & Related papers  (2024-10-16T06:14:15Z)
• Prompt-Driven Temporal Domain Adaptation for Nighttime UAV Tracking [21.09039345888337]
  Nighttime UAV tracking under low-illuminated scenarios has achieved great progress by domain adaptation (DA) Previous DA training-based works are deficient in narrowing the discrepancy of temporal contexts for UAV trackers. This work proposes a prompt-driven temporal domain adaptation training framework to fully utilize temporal contexts for challenging nighttime UAV tracking.
  arXiv  Detail & Related papers  (2024-09-27T08:12:28Z)
• Exploring Reliable Matching with Phase Enhancement for Night-time Semantic Segmentation [58.180226179087086]
  We propose a novel end-to-end optimized approach, named NightFormer, tailored for night-time semantic segmentation. Specifically, we design a pixel-level texture enhancement module to acquire texture-aware features hierarchically with phase enhancement and amplified attention. Our proposed method performs favorably against state-of-the-art night-time semantic segmentation methods.
  arXiv  Detail & Related papers  (2024-08-25T13:59:31Z)
• 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)
• Disentangled Contrastive Image Translation for Nighttime Surveillance [87.03178320662592]
  Nighttime surveillance suffers from degradation due to poor illumination and arduous human annotations. Existing methods rely on multi-spectral images to perceive objects in the dark, which are troubled by low resolution and color absence. We argue that the ultimate solution for nighttime surveillance is night-to-day translation, or Night2Day. This paper contributes a new surveillance dataset called NightSuR. It includes six scenes to support the study on nighttime surveillance.
  arXiv  Detail & Related papers  (2023-07-11T06:40:27Z)
• Tracker Meets Night: A Transformer Enhancer for UAV Tracking [20.74868878876137]
  spatial-channel Transformer-based low-light enhancer is proposed and plugged prior to tracking approaches. To achieve semantic-level low-light enhancement targeting the high-level task, novel spatial-channel attention module is proposed. In the enhancement process, SCT denoises and illuminates nighttime images simultaneously.
  arXiv  Detail & Related papers  (2023-03-20T09:18:52Z)
• 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)
• Unsupervised Learning of Accurate Siamese Tracking [68.58171095173056]
  We present a novel unsupervised tracking framework, in which we can learn temporal correspondence both on the classification branch and regression branch. Our tracker outperforms preceding unsupervised methods by a substantial margin, performing on par with supervised methods on large-scale datasets such as TrackingNet and LaSOT.
  arXiv  Detail & Related papers  (2022-04-04T13:39:43Z)
• Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer [19.33490492872067]
  We propose a framework to alleviate the accuracy decline when object detection is taken to adverse conditions by using image translation method. To alleviate the detail corruptions caused by Generative Adversarial Networks (GANs), we propose to utilize Kernel Prediction Network (KPN) based method to refine the nighttime to daytime image translation.
  arXiv  Detail & Related papers  (2021-05-11T13:18:50Z)
• Robust Object Detection via Instance-Level Temporal Cycle Confusion [89.1027433760578]
  We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf) For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision.
  arXiv  Detail & Related papers  (2021-04-16T21:35:08Z)
• 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)

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.