Background subtraction in real applications: Challenges, current models and future directions

Robust principal component analysis?

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Adaptive background mixture models for real-time tracking

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Non-parametric Model for Background Subtraction

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Wallflower: principles and practice of background maintenance

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A Bayesian computer vision system for modeling human interactions

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Efficient adaptive density estimation per image pixel for the task of background subtraction

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A survey on deep learning and its applications

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Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

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Deep Dense Multi-Scale Network for Snow Removal Using Semantic and Depth Priors

Zhang, K., Li, R., Yu, Y., Luo, W., & Li, C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3104166 · Cited by 387

Rank-One Prior: Real-Time Scene Recovery

Liu, J., Liu, R. W., Sun, J., & Zeng, T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3226276 · Cited by 163

Moving objects detection with a moving camera: A comprehensive review

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Object Detection in Traffic Videos: A Survey

Ghahremannezhad, H., Shi, H., & Liu, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3258683 · Cited by 126

BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction

Tezcan, M. O., Ishwar, P., & Konrad, J. (2021) · IEEE Access · doi:10.1109/access.2021.3071163 · Cited by 114

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

Chen, Y., Li, W., Wang, L., Hu, J., & Ye, M. (2020) · IEEE Access · doi:10.1109/access.2020.3021795 · Cited by 88

Background subtraction for moving object detection: explorations of recent developments and challenges

Kalsotra, R., & Arora, S. (2021) · The Visual Computer · doi:10.1007/s00371-021-02286-0 · Cited by 91

Foreground Gating and Background Refining Network for Surveillance Object Detection

Fu, Z., Chen, Y., Yong, H., Jiang, R., Lei Zhang, A., & Hua, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2922095 · Cited by 78

NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation

Fu, L., Vutukur, S. R., Yu, H., Shugurov, I., Busam, B., Yang, S., & Ilić, S. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00226 · Cited by 63

Encoder and decoder network with ResNet-50 and global average feature pooling for local change detection

Panda, M. K., Sharma, A., Bajpai, V., Subudhi, B. N., Veerakumar, T., & Jakhetiya, V. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103501 · Cited by 60

VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction

Wang, H., Li, Q., Sun, H., Chen, Z., Hao, Y., Peng, J., Yuan, Z., Fu, J., & Jiang, Y. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3221995 · Cited by 61

Human detection techniques for real time surveillance: a comprehensive survey

Ansari, M. A., & Singh, D. K. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10103-4 · Cited by 57

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra, R., & Arora, S. (2019) · IEEE Access · doi:10.1109/access.2019.2914961 · Cited by 55

Computer vision approaches for detecting missing barricades

Chian, E. Y. T., Fang, W., Goh, Y. M., & Tian, J. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103862 · Cited by 52

Real-Time Semantic Background Subtraction

Cioppa, A., Droogenbroeck, M. V., & Braham, M. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190838 · Cited by 52

The Emerging Field of Graph Signal Processing for Moving Object Segmentation

Giraldo, J. H., Javed, S., Sultana, M., Jung, S. K., & Bouwmans, T. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-81638-4_3 · Cited by 52

Multi‐frame based adversarial learning approach for video surveillance

Patil, P. W., Dudhane, A. A., Chaudhary, S., & Murala, S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108350 · Cited by 45

A Method for Detection of Small Moving Objects in UAV Videos

Stojnić, V., Risojević, V., Muštra, M., Jovanović, V., Filipi, J., Kezić, N., & Babić, Z. (2021) · Remote Sensing · doi:10.3390/rs13040653 · Cited by 39

High-Resolution Drone Detection Based on Background Difference and SAG-YOLOv5s

Lv, Y., Ai, Z., Chen, M., Gong, X., Wang, Y., & Lu, Z. (2022) · Sensors · doi:10.3390/s22155825 · Cited by 44

Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos

Giraldo, J. H., Javed, S., Werghi, N., & Bouwmans, T. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00030 · Cited by 36

A Resource-Efficient CNN-Based Method for Moving Vehicle Detection

Charouh, Z., Ezzouhri, A., Ghogho, M., & Guennoun, Z. (2022) · Sensors · doi:10.3390/s22031193 · Cited by 33

Non-Destructive Testing of Composite Fiber Materials With Hyperspectral Imaging—Evaluative Studies in the EU H2020 FibreEUse Project

Yan, Y., Ren, J., Zhao, H., Windmill, J. F. C., Ijomah, W., de Wit, J., & von Freeden, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3155745 · Cited by 33

GraphBGS: Background Subtraction via Recovery of Graph Signals

Giraldo, J. H., & Bouwmans, T. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412999 · Cited by 36

An improved statistical approach for moving object detection in thermal video frames

Rai, M., Sharma, R., Satapathy, S. C., Yadav, D. K., Maity, T., & Yadav, R. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11548-x · Cited by 31

ZBS: Zero-Shot Background Subtraction via Instance-Level Background Modeling and Foreground Selection

An, Y., Zhao, X., Yu, C., Gu, H., Zhao, C., Tang, M., & Wang, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00615 · Cited by 31

Deep Fusion of Appearance and Frame Differencing for Motion Segmentation

Ellenfeld, M., Moosbauer, S., Cardenes, R., Klauck, U., & Teutsch, M. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00490 · Cited by 28

Bio-Inspired Small Target Motion Detection With Spatio-Temporal Feedback in Natural Scenes

Wang, H., Zhong, Z., Fang, L., Peng, J., & Yue, S. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3345153 · Cited by 27

End-to-End Recurrent Generative Adversarial Network for Traffic and Surveillance Applications

Patil, P. W., Dudhane, A. A., & Murala, S. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3043575 · Cited by 27

Low-rank tensor recovery via non-convex regularization, structured factorization and spatio-temporal characteristics

Yu, Q., & Yang, M. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109343 · Cited by 27

FLAME: fire detection in videos combining a deep neural network with a model-based motion analysis

Gragnaniello, D., Greco, A., Sansone, C., & Vento, B. (2025) · Neural computing & applications (Print) · doi:10.1007/s00521-024-10963-z · Cited by 31

Robust moving object detection based on fusing Atanassov's Intuitionistic 3D Fuzzy Histon Roughness Index and texture features

Giveki, D. (2021) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2021.04.007 · Cited by 25

Chicken Behavior Analysis for Surveillance in Poultry Farms

Mohialdin, A., Elbarrany, A. M., & Atia, A. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01403106 · Cited by 25

FPPNet: A Fixed-Perspective-Perception Module for Small Object Detection Based on Background Difference

Liu, W., Zhou, B., Wang, Z., Yu, G., & Yang, S. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3263539 · Cited by 25

Inductive Graph Neural Networks for Moving Object Segmentation

Prummel, W., Giraldo, J. H., Zakharova, A., & Bouwmans, T. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222668 · Cited by 24

Deep Learning for Low-Light Vision: A Comprehensive Survey

Zhao, Q., Li, G., He, B., & Shen, R. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3566647 · Cited by 27

Meta-Knowledge Learning and Domain Adaptation for Unseen Background Subtraction

Zhang, J., Zhang, X., Zhang, Y., Duan, Y., Li, Y., & Pan, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3122102 · Cited by 24

Autoencoder-based background reconstruction and foreground segmentation with background noise estimation

Sauvalle, B., & Fortelle, A. D. L. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00326 · Cited by 23

A monocular-based framework for accurate identification of spatial-temporal distribution of vehicle wheel loads under occlusion scenarios

Xu, B., Liu, X., Feng, G., & Liu, C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107972 · Cited by 22

A Fast Lightweight 3D Separable Convolutional Neural Network With Multi-Input Multi-Output for Moving Object Detection

Hou, B., Liu, Y., Ling, N., Liu, L., & Ren, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3123975 · Cited by 22

Universal Background Subtraction Based on Arithmetic Distribution Neural Network

Zhao, C., Hu, K., & Basu, A. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3162961 · Cited by 23

Keypoint detection-based and multi-deep learning model integrated method for identifying vehicle axle load spatial-temporal distribution

Xu, B., & Liu, C. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102688 · Cited by 22

Kernel-Induced Possibilistic Fuzzy Associate Background Subtraction for Video Scene

Subudhi, B. N., Panda, M. K., Veerakumar, T., Jakhetiya, V., & Esakkirajan, S. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3137306 · Cited by 21

Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects

Jiang, R., Zhu, R., Su, H., Li, Y., Xie, Y., & Zou, W. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1378-4 · Cited by 22

Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system

Dou, H., Chen, S., Xu, F., Liu, Y. Y., & Zhao, H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0314817 · Cited by 29

Linear dynamical systems approach for human action recognition with dual-stream deep features

Du, Z., & Mukaidani, H. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02367-6 · Cited by 20

Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes

Zhang, Y., Zheng, W., Leng, K., & Li, H. (2020) · IEEE Access · doi:10.1109/access.2020.3009104 · Cited by 20

A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning

Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24

Aerial Autonomy Under Adversity: Advances in Obstacle and Aircraft Detection Techniques for Unmanned Aerial Vehicles

Randieri, C., Ganesh, S., Raj, R. D. A., Yanamala, R. M. R., Pallakonda, A., & Napoli, C. (2025) · Drones · doi:10.3390/drones9080549 · Cited by 22

Fast-D: When Non-Smoothing Color Feature Meets Moving Object Detection in Real-Time

Hossain, M. A., Hossain, M. I., Hossain, M. D., Thu, N. T., & Huh, E. (2020) · IEEE Access · doi:10.1109/access.2020.3030108 · Cited by 18

Improved Background Subtraction-based Moving Vehicle Detection by Optimizing Morphological Operations using Machine Learning

Charouh, Z., Ghogho, M., & Guennoun, Z. (2019) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista.2019.8778263 · Cited by 18

Multimodal Unrolled Robust PCA for Background Foreground Separation

Markowitz, S., Snyder, C., Eldar, Y. C., & Do, M. N. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3172851 · Cited by 17

Online Tensor Robust Principal Component Analysis

Salut, M. M., & Anderson, D. V. (2022) · IEEE Access · doi:10.1109/access.2022.3186364 · Cited by 17

Extracting Unambiguous Drone Signature Using High-Speed Camera

Dizeu, F. B. D., Picard, M., Drouin, M.-A., & Gagné, G. (2022) · IEEE Access · doi:10.1109/access.2022.3170481 · Cited by 16

AGVS: A New Change Detection Dataset for Airport Ground Video Surveillance

Zhang, X., Shu, C., Li, S., Wu, C., & Liu, Z. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3184978 · Cited by 16

Annotation and Benchmarking of a Video Dataset under Degraded Complex Atmospheric Conditions and Its Visibility Enhancement Analysis for Moving Object Detection

Roy, S. D., & Bhowmik, M. K. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2991191 · Cited by 16

Video Snow Removal Based on Self-Adaptation Snow Detection and Patch-Based Gaussian Mixture Model

Yang, B., Jia, Z., Yang, J., & Kasabov, N. K. (2020) · IEEE Access · doi:10.1109/access.2020.3020619 · Cited by 16

Unsupervised moving object segmentation using background subtraction and optimal adversarial noise sample search

Sultana, M., Mahmood, A., & Jung, S. K. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108719 · Cited by 16

Optical flow-based branch segmentation for complex orchard environments

You, A., Grimm, C. M., & Davidson, J. R. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982017 · Cited by 15

Graph-based Moving Object Segmentation for underwater videos using semi-supervised learning

Kapoor, M., Prummel, W., Giraldo, J. H., Subudhi, B. N., Zakharova, A., Bouwmans, T., & Bansal, A. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104290 · Cited by 16

Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection

Matczak, G., & Mazurek, P. (2021) · Remote Sensing · doi:10.3390/rs13050870 · Cited by 15

Mixed Stage Partial Network and Background Data Augmentation for Surveillance Object Detection

Chen, P.-Y., Hsieh, J.-W., Gochoo, M., & Chen, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3206709 · Cited by 14

Small-Object Detection for UAV-Based Images

Yu, M., & Leung, H. (2023) · IEEE International Systems Conference (SysCon) · doi:10.1109/syscon53073.2023.10131084 · Cited by 16

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

Li, C., Sun, H., Wang, C., Chen, S., Liu, X., Zhang, Y., Ren, N., & Tong, D. (2024) · Applied Sciences · doi:10.3390/app14010435 · Cited by 16

How to track and segment fish without human annotations: a self-supervised deep learning approach

Saleh, A., Sheaves, M. J., Jerry, D. R., & Azghadi, M. R. (2024) · Pattern Analysis and Applications · doi:10.1007/s10044-024-01227-6 · Cited by 15

Background Subtraction Based on GAN and Domain Adaptation for VHR Optical Remote Sensing Videos

Yu, W., Bai, J., & Jiao, L. (2020) · IEEE Access · doi:10.1109/access.2020.3004495 · Cited by 14

Dynamic Background Subtraction Using Least Square Adversarial Learning

Sultana, M., Mahmood, A., Bouwmans, T., & Jung, S. K. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9191235 · Cited by 14

Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

Yasir, M. A., & Ali, Y. H. (2021) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v17i02.18013 · Cited by 14

Computer Vision: A Review of Detecting Objects in Videos – Challenges and Techniques

Hammoudeh, M. A. A., Alsaykhan, M., Alsalameh, R., & Althwaibi, N. (2022) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v18i01.27577 · Cited by 14

Proposing a new feature descriptor for moving object detection

Giveki, D., Soltanshahi, M. A., & Yousefvand, M. (2020) · Optik · doi:10.1016/j.ijleo.2020.164563 · Cited by 13

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

Pamuła, T., & Pamuła, W. (2021) · Sensors · doi:10.3390/s21186281 · Cited by 13

G-LBM: Generative Low-Dimensional Background Model Estimation from Video Sequences

Rezaei, B., Farnoosh, A., & Ostadabbas, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58610-2_18 · Cited by 14

HSMD: An Object Motion Detection Algorithm Using a Hybrid Spiking Neural Network Architecture

Machado, P., Oikonomou, A., Ferreira, J. F., & McGinnity, T. M. (2021) · IEEE Access · doi:10.1109/access.2021.3111005 · Cited by 13

Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection

Zhang, X., Xiao, T., Ji, G.-P., Wu, X., Fu, K., & Zhao, Q. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3565879 · Cited by 17

Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data

Sabih, M., Farid, M. S., Ejaz, M., Husam, M., Khan, M. H., & Farooq, U. (2023) · Applied System Innovation · doi:10.3390/asi6050088 · Cited by 12

Robust and efficient FISTA-based method for moving object detection under background movements

Amoozegar, M., Akbarizadeh, M., & Bouwmans, T. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111765 · Cited by 12

Visual Navigation Systems for Maritime Smart Ships: A Survey

Wang, Y., Chen, X., Wu, Y., Zhao, J., Postolache, O. A., & Liu, S. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12101781 · Cited by 12

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

Kowalczyk, P., Komorkiewicz, M., Skruch, P., & Szelest, M. (2022) · IEEE Access · doi:10.1109/access.2022.3145192 · Cited by 13

A Survey of Efficient Deep Learning Models for Moving Object Segmentation

Hou, B., Liu, Y., Ling, N., Ren, Y., & Liu, L. (2023) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.00000140 · Cited by 12

ANIMAL DETECTION USING A SERIES OF IMAGES UNDER COMPLEX SHOOTING CONDITIONS

Зотин, А. Г., & Проскурин, А. В. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w12-249-2019 · Cited by 12

A Tensor Foreground-Background Separation Algorithm Based on Dynamic Dictionary Update and Active Contour Detection

Pei, S., Li, L., Ye, L., & Dong, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2992494 · Cited by 11

Vehicle Route Tracking System based on Vehicle Registration Number Recognition using Template Matching Algorithm

Kiew, L. C., Muzahid, A. J. M., & Kamarulzaman, S. F. (2021) · International Conference on Software Engineering and Computer Systems · doi:10.1109/icsecs52883.2021.00052 · Cited by 11

Asynchronous Semantic Background Subtraction

Cioppa, A., Braham, M., & Droogenbroeck, M. V. (2020) · Journal of Imaging · doi:10.3390/jimaging6060050 · Cited by 11

Video Background/Foreground Separation Model Based on Non-Convex Rank Approximation RPCA and Superpixel Motion Detection

Wang, Y., Wei, H., Ding, X., & Tao, J. (2020) · IEEE Access · doi:10.1109/access.2020.3018705 · Cited by 11

TensorMoG: A Tensor-Driven Gaussian Mixture Model with Dynamic Scene Adaptation for Background Modelling

Ha, S. V., Chung, N. M., Phan, H. N., & Nguyen, T.-C. (2020) · Sensors · doi:10.3390/s20236973 · Cited by 11

Denoising-Based Turbo Message Passing for Compressed Video Background Subtraction

Xue, Z., Yuan, X., & Yang, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3055063 · Cited by 11

Underwater Pipeline Oil Spill Detection Based on Structure of Root and Branch Cells

Song, H., Song, J., & Ren, P. (2020) · Journal of Marine Science and Engineering · doi:10.3390/jmse8121016 · Cited by 11

Parking detection system using background subtraction and HSV color segmentation

Pratomo, A. H., Kaswidjanti, W., Nugroho, A. S., & Saifullah, S. (2021) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v10i6.3251 · Cited by 11

DFC-D: A dynamic weight-based multiple features combination for real-time moving object detection

Hossain, M. A., Hossain, M. I., Hossain, M. D., & Huh, E. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12446-6 · Cited by 11

Human Detection in Surveillance Videos Based on Fine-Tuned MobileNetV2 for Effective Human Classification

Bouafia, Y., Guezouli, L., & Lakhlef, H. (2022) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-022-00512-6 · Cited by 11

Background memory‐assisted zero‐shot video object segmentation for unmanned aerial and ground vehicles

Yun, K., Kim, H., Bae, K., & Moon, J. (2023) · ETRI Journal · doi:10.4218/etrij.2023-0115 · Cited by 9

Automatic Background Filtering for Cooperative Perception Using Roadside LiDAR

Liu, J., Zhao, J., Guo, J., Zou, C., Yin, X., Cheng, X., & Khan, F. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3342178 · Cited by 10

Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning

Santos, N. P. (2024) · Robotics · doi:10.3390/robotics13080114 · Cited by 10

Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement

Ma, T. J., & Anderson, R. J. (2023) · Sensors · doi:10.3390/s23063314 · Cited by 10

Unsupervised Adversarial Learning for Dynamic Background Modeling

Sultana, M., Mahmood, A., Bouwmans, T., & Jung, S. K. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-4818-5_19 · Cited by 10

ABGS Segmenter: pixel wise adaptive background subtraction and intensity ratio based shadow removal approach for moving object detection

Agrawal, S., & Natu, P. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04972-9 · Cited by 10

Efficient vehicle detection for traffic video-based intelligent transportation systems applications using recurrent architecture

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