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Unsupervised inner-point-pairs model for unseen-scene and online moving object detection

VISUAL COMPUTER 2023年第4期39卷 1391-1407页

作者： Zhao, Xinyue Wang, Guangli He, Zaixing Liang, Dong Zhang, Shuyou Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab CAD&CG Hangzhou 310027 Zhejiang Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China

moving object detection in complex environments is a challenging task. Recently, many deep learning-based methods have improved the detecting accuracy. However, because of the supervised learning nature, these methods cannot deal with unseen scene, where manually generated groundtruth of training data is unavailable. Furthermore, due to the limitation of generality, their online detection performance is not satisfactory, where the scene changes a lot over time. In this paper, we propose a new unsupervised method for moving object detection, which performs well in both seen and unseen scene, offline and online detection. It can better adapt to changes in environmental lighting and has good tolerance for dynamic backgrounds in the environment. Based on the analysis of the reflections of objects in a scene, the proposed method not only processes images at the pixel level but also takes into account the characteristics at the object level. We distinguish between the ordinary point areas and complex point areas in an image and process them separately to improve the detection accuracy. The proposed method is tested using several challenging datasets, and the results show its effectiveness and applicability in complex situations.

关键词： moving object detection Inner-point-pairs Dynamic background model Unseen-scene and online detection

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moving object detection IN NOISY VIDEO SEQUENCES USING DEEP CONVOLUTIONAL DISENTANGLED REPRESENTATIONS 29

MOVING OBJECT DETECTION IN NOISY VIDEO SEQUENCES USING DEEP ...

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IEEE International Conference on Image Processing (ICIP)

作者： Garcia-Gonzalez, Jorge Luque-Baena, Rafael M. Ortiz-de-Lazcano-Lobato, Juan M. Lopez-Rubio, Ezequiel Univ Malaga Dept Comp Languages & Comp Sci Biomed Res Inst Malaga IBIMA Malaga Spain

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Noise robustness is crucial when approaching a moving detection problem since image noise is easily mistaken for movement. In order to deal with the noise, deep denoising autoencoders are commonly proposed to be applied on image patches with an inherent disadvantage with respect to the segmentation resolution. In this work, a fully convolutional autoencoder-based moving detection model is proposed in order to deal with noise with no patch extraction required. Different autoencoder structures and training strategies are also tested to get insights into the best network design approach.

关键词： moving object detection Foreground Segmentation Autoencoders

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moving object detection with Single moving Camera and IMU Sensor using Mask R-CNN Instance Image Segmentation

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2021年第6期22卷 1049-1059页

作者： Jung, Sukwoo Cho, Youngmok Lee, KyungTaek Chang, Minho Korea Univ Dept Mech Engn Seoul South Korea Korea Elect Technol Inst Contents Convergence Res Ctr Seoul South Korea

This paper describes a new method for the moving object detection using the IMU sensor and instance image segmentation. In the proposed method, the feature points are extracted by the detector, and the initial fundamental matrix is calculated from the IMU data. Next, the epipolar line is used to classify the extracted feature points. From the background feature point matching, fundamental matrix is calculated iteratively to minimize the error of classification. After the feature point classification, image segmentation is used to enhance the quality of the classification result. The proposed method is implemented and tested with real-world driving videos, and compared with the previous works.

关键词： moving camera Motion estimation moving object detection Deep learning

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moving object detection based on bioinspired background subtraction

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BIOINSPIRATION & BIOMIMETICS 2024年第5期19卷 056002-056002页

作者： Zheng, Zhuanzhen Guo, Aike Wu, Zhihua Shanghai Univ Sch Life Sci Shanghai 200444 Peoples R China Beijing Normal Univ Zhuhai Adv Inst Nat Sci Int Acad Ctr Complex Syst Zhuhai 519087 Guangdong Peoples R China

Flying insects rely mainly upon visual motion to detect and track objects. There has been a lot of research on fly inspired algorithms for object detection, but few have been developed based on visual motion alone. One of the daunting difficulties is that the neural and circuit mechanisms underlying the foreground-background segmentation are still unclear. Our previous modeling study proposed that the lobula held parallel pathways with distinct directional selectivity, each of which could retinotopically discriminate figures moving in its own preferred direction based on relative motion cues. The previous model, however, did not address how the multiple parallel pathways gave the only detection output at their common downstream. Since the preferred directions of the pathways along either horizontal or vertical axis were opposite to each other, the background moving in the opposite direction to an object also activated the corresponding lobula pathway. Indiscriminate or ungated projection from all the pathways to their downstream would mix objects with the moving background, making the previous model fail with non-stationary background. Here, we extend the previous model by proposing that the background motion-dependent gating of individual lobula projections is the key to object detection. Large-field lobula plate tangential cells are hypothesized to perform the gating to realize bioinspired background subtraction. The model is shown to be capable of implementing a robust detection of moving objects in video sequences with either a moving camera that induces translational optic flow or a static camera. The model sheds light on the potential of the concise fly algorithm in real-world applications.

关键词： moving object detection motion detectors for biological vision background motion estimation fly inspired background subtraction video datasets

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FPGA-based moving object detection Leveraging Frame Difference and Morphological Filtering 2

FPGA-based Moving Object Detection Leveraging Frame Differen...

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2nd International Conference on Computer Vision and Intelligent Technology

作者： Wang, Yuexin Jing, Xiaochen Wang, Wenhao Zhu, Mengli Zhu, Dongchen Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Bion Vis Syst Lab Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798331540050;9798331540043

moving object detection plays a significant role in video surveillance. However, existing moving object detection methods often rely on software implementations, which means low real-time performance and high power consumption. This paper's core detection algorithm employs a frame difference method that is enhanced by morphological filtering. Additionally, we propose an architecture that integrates FPGA(Field Programmable Gate Array) and ARM(Advanced RISC Machine), fully leveraging the parallel computing advantages of FPGA and the high processing efficiency of ARM. The system utilizes a ZYNQ7000 SoC, coupled with an OV7725 camera for image capture and DDR3 SDRAM for data caching, to address the challenges of high-speed data processing and low power consumption. Experimental results show that the system meets the requirements for high real-time performance and low power consumption with a frame rate of 85.9375 frames per second and a total power consumption of 1.101 W.

关键词： FPGA frame difference method moving object detection real-time video processing

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A robust RGB-D visual odometry with moving object detection in dynamic indoor scenes

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IET Cyber-Systems and Robotics 2023年第1期5卷 79-88页

作者： Xianglong Zhang Haiyang Yu Yan Zhuang College of Mechanical and Electronic Engineering Dalian Minzu UniversityDalianChina School of Control Science and Engineering Dalian University of TechnologyDalianChina

Simultaneous localisation and mapping(SLAM)are the basis for many robotic *** the front end of SLAM,visual odometry is mainly used to estimate camera *** dynamic scenes,classical methods are deteriorated by dynamic objects and cannot achieve satisfactory *** order to improve the robustness of visual odometry in dynamic scenes,this paper proposed a dynamic region detection method based on RGBD ***,all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint ***,the depth image is clustered using the K-Means *** classified feature points are mapped to the clustered depth image,and a dynamic or static label is assigned to each cluster according to the number of dynamic feature ***,a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image,and the feature points covered by the mask are all *** remaining static feature points are applied to estimate the camera ***,some experimental results are provided to demonstrate the feasibility and performance.

关键词： dynamic indoor scenes moving object detection RGB-D SLAM visual odometry

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CTFU-Net:CNN-Transformer Fusion U-shaped Network for moving object detection 3

CTFU-Net:CNN-Transformer Fusion U-shaped Network for Moving ...

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3rd International Conference on Image Processing and Media Computing (ICIPMC)

作者： Xia, Tingting Yang, Yizhong Hefei Univ Technol Sch Microelect Hefei Peoples R China

ISBN: (纸本)9798350386660;9798350386677

The technique of extracting changing areas or moving objects from image sequences is known as moving object detection. The two most popular deep learning frameworks available now are convolutional neural network (CNN) and Transformer. CNN's strong local feature extraction abilities enable it to effectively capture local semantic information, while Transformers excel in modeling long-range semantic information. Consequently, we propose an innovative method for detecting moving objects by leveraging the benefits of both CNN and Transformer: CNN-Transformer fusion U-shaped network (CTFUNet). The main concept is to achieve information sharing between two different types of branches by hierarchically fusing the local feature information and the global context information through the feature interaction module (FIM). Furthermore, we also present a hybrid loss function to alleviate the foreground and background class imbalance problem. The experimental results demonstrate that CTFU-Net can extract more complete and clearer moving objects with better visual performance compared to the state-of-the-art methods.

关键词： moving object detection Transformer multi-head self-attention feature fusion hybrid loss

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

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第22期81卷 32549-32580页

作者： Hossain, Md Alamgir Hossain, Md Imtiaz Hossain, Md Delowar Huh, Eui-Nam Kyung Hee Univ Dept Comp Sci & Engn Global Campus Yongin South Korea

Real-time moving object detection is an emerging method in Industry 5.0, that is applied in video surveillance, video coding, human-computer interaction, IoT, robotics, smart home, smart environment, edge and fog computing, cloud computing, and so on. One of the main issues is accurate moving object detection in real-time in a video with challenging background scenes. Numerous existing approaches used multiple features simultaneously to address the problem but did not consider any adaptive/dynamic weight factor to combine these feature spaces. Being inspired by these observations, we propose a background subtraction-based real-time moving object detection method, called DFC-D. This proposal determines an adaptive/dynamic weight factor to provide a weighted fusion of non-smoothing color/gray intensity and non-smoothing gradient magnitude. Moreover, the color-gradient background difference and segmentation noise are employed to modify thresholds and background samples. Our proposed solution achieves the best trade-off between detection accuracy and algorithmic complexity on the benchmark datasets while comparing with the state-of-the-art approaches.

关键词： moving object detection Classification Change detection Foreground segmentation Background subtraction

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An Overview of moving object detection Using YOLO Deep Learning Models 2

An Overview of Moving Object Detection Using YOLO Deep Learn...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dwivedi, Upendra Joshi, Kireet Shukla, Surendra Kumar Rajawat, Anand Singh G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India SVKM S Nmims Mpstme Department of Computer Science and Engineering Shirpur Campus Shirpur India School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9798350371055

Computer vision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real time video streaming. In Computer Vision and image processing detecting object from images and videos has been topic of extensive research. This is accomplished by applying various computer vision techniques to analyze the visual data and determine class of objects from image and videos files. One widespread approach to object detection is using the deep learning models. YOLO (You Only Look Once) is a convolutional neural network that offers a fast and efficient solution to object detection using deep learning. It enable computer to handle real-time detection of objects in video frames and accurately locate and classify moving objects. YOLO is able to simultaneously detect and classify objects in an efficient manner using convolutional neural networks. © 2024 IEEE.

关键词： Deep Learning Image Processing Motion detection moving object detection YOLO

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Learning dynamic background for weakly supervised moving object detection

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IMAGE AND VISION COMPUTING 2022年 121卷 104425-104425页

作者： Zhang, Zhijun Chang, Yi Zhong, Sheng Yan, Luxin Zou, Xu Huazhong Univ Sci & Technol Wuhan Peoples R China Pengcheng Lab Shenzhen Peoples R China

moving object detection (MOD) aims at extracting foreground moving objects in videos from static cameras. While low-rank based approaches have achieved impressive success in the MOD task, their performance remains limited on dynamics background scenes. The main reason is that dynamic clutters, e.g., swaying leaves and rippers, are easy to mix up with moving objects in the decomposition model which simply classify the sparse noise as foregrounds. In order to improve the generalization ability of low-rank based moving object detectors, we suggest adding an explicit dynamic clutter component in the decomposition framework with realistic dynamic background modeling. Then the dynamic clutter can be learned through object-free video data due to their self similarity across time and space. Thus, the moving objects can be naturally separated by a tensor-based decomposition model which formulates the static background by a unidirectional low-rank tensor, learns the dynamic clutter by a two-stream neural network, and constrains moving objects with spatiotemporal continuity. To further provide a more accurate object detection result, an objectness prior is embedded into our model in an attention manner. Extensive experimental results on the challenging datasets of dynamic background clearly demonstrate the superior performance of our model over the state-of-the-art in terms of quantitative metrics and visual quality.(c) 2022 Elsevier B.V. All rights reserved.

关键词： moving object detection Dynamic background Data-driven discriminative prior Low-rank framework

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