Remote Sensing Object Tracking With Deep Reinforcement Learning Under Occlusion

Cui, Yanyu; Hou, Biao; Wu, Qian; Ren, Bo; Wang, Shuang; Jiao, Licheng

doi:10.1109/tgrs.2021.3096809

Cited by 33 publications

(18 citation statements)

References 56 publications

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“…Additionally, by exploring the temporal and spatial context, the object appearance model, and the motion vector from occluded targets, Ref. [84] designed a Reinforcement learning (RL)-based approach to enhance the tracking performance under complete occlusion. In addition, Ref.…”

Section: Dl-based Tracking Methodsmentioning

confidence: 99%

“…Meanwhile, due to the advantages in time-series image processing, RN-based approaches have shown their potential for advanced tasks, such as long-term tracking or tracking with occlusion. [82] 2021 A two-branch LSTM PN-based tracking [83] 2021 PN to predict the location probability of the target Combined SN and RN [80] 2022 SRN followed by FTM RL-based tracking [84] 2022 RL to track objects under occlusion GC-based tracking [85] 2022 Tracking via GC-based multitask reasoning…”

Section: Dl-based Tracking Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on wpafb videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower ds are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-ds group and the Hard-ds group, respectively.

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Section: Dl-based Tracking Methodsmentioning

confidence: 99%

Section: Dl-based Tracking Methodsmentioning

confidence: 99%

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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“…( multiplied with the correlation filter, and then inverse Fourier transform, which improves the computational efficiency [218]. Du et al [219] used the kernelized correlation filter (KCF) tracker [220], a classical algorithm in correlation filtering, for remote sensing video object tracking.…”

Section: Multi-oriented Object Representationmentioning

confidence: 99%

“…To exploit the learning ability of the neural network, deep reinforcement learning is also introduced to track objects in satellite videos. Cui et al [218] proposed an action decision-occlusion handling network to leverage the occlusion information and drive actions under occlusion.…”

Section: Multi-oriented Object Representationmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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“…The advantages of such combinations are investigated in computer vision tasks such as object localization [63][64][65][66], and visual tracking [67][68][69][70][71]. Combining hard attention with deep neural networks proposes a great solution to the trade-off between accuracy and performance.…”

Section: Related Workmentioning

confidence: 99%

Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model

Mohammadi¹,

Nazerfard²

2022

Preprint

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Empowering automated violence monitoring and surveillance systems amid the growing social violence and extremist activities worldwide could keep communities safe and save lives. The questionable reliability of human monitoring personnel and the increasing number of surveillance cameras makes automated artificial intelligence-based solutions compelling. Improving the current state-of-the-art deep learning approaches to video violence recognition to higher levels of accuracy and performance could enable surveillance systems to be more reliable and scalable.The main contribution of the proposed deep reinforcement learning method is to achieve state-of-the-art accuracy on RWF, Hockey, and Movies datasets while removing some of the computationally expensive processes and input features used in the previous solutions. The implementation of hard attention using a semisupervised learning method made the proposed method capable of rough violence localization and added increased agent interpretability to the violence detection system.

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Remote Sensing Object Tracking With Deep Reinforcement Learning Under Occlusion

Cited by 33 publications

References 56 publications

Object Tracking Based on Satellite Videos: A Literature Review

Object Tracking Based on Satellite Videos: A Literature Review

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model

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