Moving objects detection methods can be classified as follows:
A - Passive Sensors (3939 papers)
A.1 - Conventional Cameras [RGB Cameras, IR Cameras, Multispectral Cameras, RGB-D Cameras, Lightfield Cameras] (3930 papers)
Consecutive Frame Difference (7 papers)
Background Subtraction (Background Subtration Website) (3923 papers)
Optical flow (papers)
A.2 - Events based Cameras (9 papers)
L. Fan, Y. Li, H. Shen, J. Li, D. Hu, "From Dense to Sparse: Low-Latency and Speed-Robust Event-Based Object Detection", IEEE Transactions on Intelligent Vehicles, March 2024.
H. Zhou, Z. Shi, H. Dong, S. Peng, Y. Chang, L. Yan "JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection", Preprint, March 2024.
S. Schaefer, D. Gehrig, D. Scaramuzza, "AEGNN: Asynchronous Event-based Graph Neural Networks ", IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2022,New Orleans, USA, 2022.
A. Mondal, R. Shashant, J. Giraldo, T. Bouwmans, A. Chowdhury, "Moving Object Detection for Event-based Vision using Graph Spectral Clustering", Workshop on "When Graph Signal Processing meets Computer Vision", ICCV 2021, Montréal, Canada, October 2021.
S. Zhang, W. Wang, H. Li, S. Zhang, "EventMD: High-Speed Moving Object Detection based on Event-based Video Frames", Pattern Recognition Letters, 2022.
J. Zhao , S. Ji, Z. Cai , Y. Zeng, Y. Wang,"Moving Object Detection and Tracking by Event Frame from Neuromorphic Vision Sensors", MDPI Biomemetics, 2022.
A. Mitrokhin, C. Fermüller, C. Parameshwara, Y. Aloimonos, "Event-Based Moving Object Detection and Tracking," 2018 IEEE International Conference on Intelligent Robots and Systems, IROS 2018, pages 1-9, 2018.
B - Active Sensors (10 papers)
B.1. Synthetic Aperture Radar (SAR) (1 paper)
S. Markowitz, C. Snyder, Y. Eldar, M. Do, "Multimodal Unrolled Robust PCA for Background Foreground Separation", Preprint, 2021. [Unrolled RPCA]
B. 2 Light Detection and Ranging (LiDAR) (9 papers)
C. Ma, X. Shi, Y. Wang, S. Song, Z. Pan, J. Hu, “MosViT: Towards Vision Transformers for moving object segmentation based on Lidar point cloud”, Measurement Science and Technology, 2024.
N. Wang, C. Shi, R. Guo, H. Lu, Z. Zheng, X. Chen,“InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data”, Preprint, 2023.
T. Zhang, P. Jin, Y. Ge, "Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection", Preprint, April 2022. [MoG]
T. Zhang, P. Jin, Y. Ge, “Multimodal Gaussian Mixture Model for Realtime Roadside LiDAR Object Detection”, Preprint, 2022. [MoG]
T. Zhang, P. Jin, “Roadside LiDAR Vehicle Detection And Tracking Using Range and Intensity Background Subtraction”, Preprint, January 2022. [DMD]
Y. Xia, Z. Sun, A. Tok, S. Ritchie, "A dense background representation method for traffic surveillance based on roadside LiDAR", Optics and Lasers in Engineering, Volume 152, May 2022. [MoG]
L. Kovacs, M. Kegl, C. Benedek, “Real-Time Foreground Segmentation For Surveillance Applications In NRCS Lidar Sequences”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, pages 45-51, 2022. [MoG]
Z. Zhang, J. Zheng, H. Xu, X. Wang, X. Fan, R. Chen, "Automatic background construction and object detection based on roadside LiDAR", IEEE Transactions on Intelligent Transportation Systems, Volume 21, No. 10 , pages 4086-4097, 2020. [Mean]
J. Zhao, H. Xu, H. Liu, J. Wu, Yi. Zheng, D. Wu, “Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors”, Transportation Research Part C: Emerging Technologies, Volume 100, pages 68-87, March 2019. [Filter]