Multivehicle Object Tracking in Satellite Video Enhanced by Slow Features and Motion Features

Wu, Jiong; Su, Xin; Yuan, Qiangqiang; Shen, Huanfeng; Zhang, Liangpei

doi:10.1109/tgrs.2021.3139121

Cited by 18 publications

(4 citation statements)

References 59 publications

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“…Aiming at tracking multiple moving objects, Ref. [60] proposed the slow feature and motion feature-guided multi-object tracking (SFMFT) method by using the slow features and motion features. Specifically, the authors developed a nonmaximum suppression (NMS) module to assist the object detection by utilizing the sensitivity of slow feature analysis to the changed pixels.…”

Section: Tbd Methodsmentioning

confidence: 99%

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: Tbd Methodsmentioning

confidence: 99%

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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“…CKDNet assists the detection of keypoints by collecting complementary information between frames and efficiently tracks densely-arranged vehicles by building a two-branch long short-term memory. Wu et al [236] present slow Feature and sotion Feature to guide the multiobject tracking, in which bounding box proposal-guided NMS modules based on SFs enhance the detection of regions of interest.…”

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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“…In order to speed up tracking, motion parameters and accumulated temporal information are used to optimize the trajectory, reduce static false alarms, and replicate bounding boxes. A comparison of this strategy to three satellite videos reveals superior results [2].…”

Section: Introductionmentioning

confidence: 97%

Abnormal Frame Extraction and Object Tracking Hybrid Machine Learning

Archana

Prasad

2023

Preprint

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The detection of events using the video data is getting popular due to the detailed information made available from the video data for multiple instances. This popularity is increasing the use of number of devices and amount of data generated from various sources, which makes the manual detection of abnormal events highly complex, and the recent research demands highly timely and highly accurate automation process. Thus, this work proposes a three-phase solution to address this problem as using a hybrid segmentation process for object detection with 97% accuracy, detection of the objects by using a pre-trained machine learning model with 98% accuracy and detection of the motion using predictive regression model with mean time of 58 ns. This proposed work has demonstrated benchmarking outcomes and showcases highly accurate detection process for making video-based surveillance safer and better choice.

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Multivehicle Object Tracking in Satellite Video Enhanced by Slow Features and Motion Features

Cited by 18 publications

References 59 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

Abnormal Frame Extraction and Object Tracking Hybrid Machine Learning

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