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检索条件"主题词=Moving Object Segmentation"

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Real-time moving object segmentation with Tracking and Tracklet Belief 43

Real-time Moving Object Segmentation with Tracking and Track...

引用

43rd Chinese Control Conference (CCC)

作者： Lyu, Mengxin Liang, Wenhao Shi, Zongying Zhong, Yisheng Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350366907;9789887581581

moving object segmentation (MOS) is a challenging but essential part for autonomous robotic systems. segmentation of moving objects provides significant information for mapping, avoiding collisions, and planning task. In this work, we address the problem of object detection and tracking in 3D LiDAR sequences. Our approach achieves robust moving object segmentation and object velocity estimation by unique object clustering and association strategy. An object-level dynamic belief is calculated to remove incorrect associations and non-moving objects. Experiment results on SemanticKITTI dataset show that our approach accurately detects moving objects and outperforms state-of-the-art MOS methods. Furthermore, we verify our approach on a self-collected campus dataset with Velodyne VLP-16. The results show that it adapts well to different sensor setups since it incorporates information of objects' motion and does not depend on training data.

关键词： moving object segmentation LiDAR Autonomous driving

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An Effective segmentation Cue for moving object segmentation from a moving Camera 7

An Effective Segmentation Cue for Moving Object Segmentation...

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7th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT) - Optoelectronics Materials and Devices for Sensing and Imaging

作者： Wang, Shaobai Wu, Mingjun Xie, Yi AVIC Luoyang Inst Electroopt Equipment Luoyang 471009 Peoples R China Luoyang Optoelect Technol Dev Ctr Luoyang 471009 Peoples R China Sci & Technol Elect Optic Control Lab Luoyang 471009 Peoples R China

ISBN: (纸本)9781628413595

To overcome the difficulty in segmenting moving objects from a video sequence taken by a freely moving camera, we propose a new segmentation cue based on a joint spatial-color representation for the foreground and background appearances. Given the segmentation result in the previous frame, two sample sets can be extracted for foreground and background, respectively. We transform the spatial locations of the samples in the two sets towards the current frame, then use these transformed samples to evaluate the foreground and background likelihood maps, and combine these maps to form a likelihood ratio map which is further exploited as a segmentation cue and integrated into a conditional random field energy function. The total conditional random field energy is minimized by the graph cut, leading to a binary mask of moving objects for each video frame. We validate the proposed segmentation cue using several video sequences taken by hand-held cameras in outdoor urban scenes and the results show the effiency of the segmentation cue.

关键词： moving object segmentation segmentation cue Conditional random field moving camera

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Efficient moving object segmentation Algorithm Based on the Improvement of Generalized Geodesic Active Contour Model 8

Efficient Moving Object Segmentation Algorithm Based on the ...

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8th IEEE International Conference on Communication Software and Networks (ICCSN)

作者： Chen, Ying Yu, Qi Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Peoples R China Xian Med Univ Inst Basic & Translat Med Xian Peoples R China

ISBN: (纸本)9781509017812

The task of moving object segmentation is to partition an object into non-overlapping regions based on intensity or texture information. However, the conventional segmentation methods suffer from false object segmentation in complex backgrounds and slow convergence. In this paper, we propose an efficient segmentation algorithm for moving object with complicated structures in real video environment. Our novel approach, which integrates an adaptive single gaussian model (SGM) with a generalized geodesic active contour (GGAC) model, is put forward to detect and segment moving objects in dynamic backgrounds. The proposed algorithm is implemented by level set method to reduce the expensive computational cost of re-initialization of the traditional level set function. By utilizing both spatial and temporal information, this integrated method is robust to complex environments. Experimental results demonstrate desirable segmentation improvement over widely used segmentation algorithms in terms of efficiency and accuracy.

关键词： moving object segmentation single gaussian model generalized geodesic active contour model level set function

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A FOUNDATION MODEL FOR GENERAL moving object segmentation IN MEDICAL IMAGES 21

A FOUNDATION MODEL FOR GENERAL MOVING OBJECT SEGMENTATION IN...

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21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Yan, Zhongnuo Han, Tong Huang, Yuhao Liu, Lian Zhou, Han Chen, Jiongquan Shi, Wenlong Cao, Yan Yang, Xin Ni, Dong Shenzhen Univ Shenzhen Univ Med Sch Natl Reg Key Technol Engn Lab Med Ultrasound Sch Biomed Engn Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Shenzhen RayShape Med Technol Inc Shenzhen Peoples R China

ISBN: (纸本)9798350313345;9798350313338

Medical image segmentation aims to delineate the anatomical or pathological structures of interest, playing a crucial role in clinical diagnosis. A substantial amount of high-quality annotated data is crucial for constructing high-precision deep segmentation models. However, medical annotation is highly cumbersome and time-consuming, especially for medical videos or 3D volumes, due to the huge labeling space and poor inter-frame consistency. Recently, a fundamental task named moving object segmentation (MOS) has made significant advancements in natural images. Its objective is to delineate moving objects from the background within image sequences, requiring only minimal annotations. In this paper, we propose the first foundation model, named iMOS, for MOS in medical images. Extensive experiments on a large multi-modal medical dataset validate the effectiveness of the proposed iMOS. Specifically, with the annotation of only one image in the sequence, iMOS can achieve satisfactory tracking and segmentation performance of moving objects throughout the entire sequence in bi-directions. We hope that iMOS can help accelerate the annotation speed of experts, and boost the development of medical foundation models.

关键词： Medical image moving object segmentation Foundation model

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An Adaptive Threshold Algorithm for moving object segmentation 1st

An Adaptive Threshold Algorithm for Moving Object Segmentati...

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1st Chinese Conference on Computer Vision (CCCV)

作者： Tian, Yumin Wang, Dan Lin, Risan Chen, Qichao Xidian Univ Sch Comp Sci & Technol Xian 710071 Shannxi Peoples R China

ISBN: (纸本)9783662485583;9783662485576

Connected region detection is usually used to obtain foreground regions from foreground image after moving object detection. In order to remove noise regions and retain true targets, a threshold that limits the circumference of foreground regions should be introduced. The method which uses the same threshold for all surveillance videos cannot handle scene changes. In this case, we propose an adaptive threshold algorithm for moving target segmentation. A strategy based on the combination of background modeling and Grabcut is presented to extract foreground objects and set an initial threshold. On the base of this, we can choose some foreground as samples and classify them by K-means clustering method. Finally, an appropriate threshold could be selected for moving object segmentation according to the classification result. Experimental results show that the proposed method has strong adaptability to various scenes and improves the accuracy oftarget segmentation.

关键词： moving object segmentation Adaptive threshold K-means clustering Image segmentation

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Boundary connectedness based video cut for moving object segmentation 3

Boundary connectedness based video cut for moving object seg...

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3rd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Ramadan, Hiba Tairi, Hamid Univ Sidi Mohamed Ben Abdellah Fac Sci Dhar Mahraz Dept Informat LIIAN Lab Fes Morocco

ISBN: (纸本)9781538605516

A new algorithm for automatic segmentation of moving objects in video based on spatio-temporal saliency and Neutro-Connectedness is presented in this paper. First, we propose a simple model to compute video saliency by combining initial saliency maps computed in spatial and temporal domains. Then, based on the detected spatiotemporal saliency map and temporal superpixels, initial background and foreground regions can be detected and taken as input of our proposed boundary connectedness based video cut (BC-video cut) to achieve moving object segmentation. Our model predicts jointly appearance models, Neutro-Connectedness, and pixel labels via an iterative energy minimization framework. Experiments show a good performance of our algorithm to segment moving objects on benchmark datasets.

关键词： spatiotemporal saliency Neutro-Connectedness boundary connectedness moving object segmentation

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CURVELET TRANSFORM BASED moving object segmentation

CURVELET TRANSFORM BASED MOVING OBJECT SEGMENTATION

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20th IEEE International Conference on Image Processing (ICIP)

作者： Khare, Manish Srivastava, Rajneesh Kumar Khare, Ashish Jeon, Moongu Univ Allahabad Dept Elect & Commun Allahabad 211002 Uttar Pradesh India Gwangju Inst Sci & Technol Sch Informat & Commun Gwangju South Korea

ISBN: (纸本)9781479923410

In this paper, we have proposed a new method for segmentation of moving objects, which is based on single change detection applied on curvelet coefficients of two consecutive frames. The wavelet transform is widely used in moving object segmentation but it can not describe curve discontinuities. Therefore we have used curvelet transform for segmentation of moving objects. The proposed method is simple and does not require any other parameter except curvelet coefficients. Results after applying the proposed method for segmentation of moving object are compared with other state-of-the-art methods in terms of visual as well as quantitative performance measures viz. Misclassification penalty, Relative position based measure and Structural content. The proposed method is found to be better than other methods.

关键词： moving object segmentation Curvelet transform Single change detection

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moving object segmentation in H.264/AVC Compressed Domain Using Ant Colony Algorithm

Moving Object Segmentation in H.264/AVC Compressed Domain Us...

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2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

作者： Wang Pei Wu Zhixia College of informatiom mechanical & electrical engineering Shanghai Normal University Shanghai China

This paper presents a novel approttch for moving object segmentation in the H.264/AVC compressed domain, which based on ant colony clustering algorithm. Firstly, the motion vector (MV) field is extracted from the H.264/AVC compressed video, and then merges motion vectors with the same characteristic. Secondly, an improved ant colony clustering algorithm is used to classify the MV field into different motion homogenous regions. Finally. the orientation histogram of the MV field and the final cluster centers are exploited to determine the moving object regions. Experimental results for several video sequences demonstrate that the proposed approach can extract moving object effectively.

关键词： H.264/AVC moving object segmentation movtion vector ant colony algorithm

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moving object segmentation Using dynamic 3D Graph Cuts and GMM

Moving Object Segmentation Using dynamic 3D Graph Cuts and G...

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2006 8th International Conference on Signal Processing

作者： Bo Li Baozong Yuan Yunda Sun Institute of Information Science Beijing Jiaotong University

It is one of the most challenging problems in computer vision how to segment moving objects *** this paper,we present a novel approach to segment moving objects with edge information and temporal information using 3D Graph cuts model when cameras is *** object segmentation is modeled as finding a minimum energy of 3D *** algorithm assigns n-links in 3D graph according to spatial gradient in same frame and temporal gradient in neighboring *** mixture model is used to assign t-links with edge difference term and shadow elimination term. Finally,a dynamic graph cuts algorithm is used to find the minimum cut of 3D graph and segments moving objects in image *** show that our approach achieves nice performance.

关键词： moving object segmentation 3D graph cuts GMM spatial gradient temporal gradient

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moving object segmentation for Video Surveillance and Conferencing Applications

Moving Object Segmentation for Video Surveillance and Confer...

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2003 International Conference on Communication Technology

作者： Falah E.Alsaqre Institute of Information Science Northern Jiaotong University Beijing 100044 China

Video surveillance and conferencing systems have an impressive spread both for their practical application and interest as research issue. The common approach used in such systems consists of a good s

关键词： moving object segmentation canny edge detector morphological operations

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