检索结果-内蒙古大学图书馆

Gradient Enhancement Techniques and Motion Consistency Constraints for moving object segmentation in 3D LiDAR Point Clouds

引用

REMOTE SENSING 2025年第2期17卷 195-195页

作者： Tang, Fangzhou Zhu, Bocheng Sun, Junren Peking Univ Sch Elect Beijing 100871 Peoples R China China Univ Min & Technol Beijing Sch Artificial Intelligence Beijing 100083 Peoples R China

The ability to segment moving objects from three-dimensional (3D) LiDAR scans is critical to advancing autonomous driving technology, facilitating core tasks like localization, collision avoidance, and path planning. In this paper, we introduce a novel deep neural network designed to enhance the performance of 3D LiDAR point cloud moving object segmentation (MOS) through the integration of image gradient information and the principle of motion consistency. Our method processes sequential range images, employing depth pixel difference convolution (DPDC) to improve the efficacy of dilated convolutions, thus boosting spatial information extraction from range images. Additionally, we incorporate Bayesian filtering to impose posterior constraints on predictions, enhancing the accuracy of motion segmentation. To handle the issue of uneven object scales in range images, we develop a novel edge-aware loss function and use a progressive training strategy to further boost performance. Our method is validated on the SemanticKITTI-based LiDAR MOS benchmark, where it significantly outperforms current state-of-the-art (SOTA) methods, all while working directly on two-dimensional (2D) range images without requiring mapping.

关键词： LiDAR point cloud moving object segmentation range image gradient enhancement motion consistency

来源：评论

学校读者我要写书评

暂无评论

Instance-Level moving object segmentation from a Single Image with Events

引用

INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年 1-22页

作者： Wan, Zhexiong Fan, Bin Hui, Le Dai, Yuchao Lee, Gim Hee Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Shaanxi Peoples R China Shaanxi Key Lab Informat Acquisit & Proc Xian 710129 Shaanxi Peoples R China Natl Univ Singapore Dept Comp Sci Singapore 117417 Singapore Peking Univ Sch Intelligence Sci & Technol Natl Key Lab Gen Artificial Intelligence Beijing 100871 Peoples R China

moving object segmentation plays a crucial role in understanding dynamic scenes involving multiple moving objects, while the difficulties lie in taking into account both spatial texture structures and temporal motion cues. Existing methods based on video frames encounter difficulties in distinguishing whether pixel displacements of an object are caused by camera motion or object motion due to the complexities of accurate image-based motion modeling. Recent advances exploit the motion sensitivity of novel event cameras to counter conventional images' inadequate motion modeling capabilities, but instead lead to challenges in segmenting pixel-level object masks due to the lack of dense texture structures in events. To address these two limitations imposed by unimodal settings, we propose the first instance-level moving object segmentation framework that integrates complementary texture and motion cues. Our model incorporates implicit cross-modal masked attention augmentation, explicit contrastive feature learning, and flow-guided motion enhancement to exploit dense texture information from a single image and rich motion information from events, respectively. By leveraging the augmented texture and motion features, we separate mask segmentation from motion classification to handle varying numbers of independently moving objects. Through extensive evaluations on multiple datasets, as well as ablation experiments with different input settings and real-time efficiency analysis of the proposed framework, we believe that our first attempt to incorporate image and event data for practical deployment can provide new insights for future work in event-based motion related works. The source code with model training and pre-trained weights is released at https://***/EvInsMOS.

关键词： Event camera Motion analysis moving object segmentation Instance segmentation Event and image fusion

来源：评论

学校读者我要写书评

暂无评论

Efficient moving object segmentation in LiDAR Point Clouds Using Minimal Number of Sweeps

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

引用

IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2025年 6卷 118-128页

作者： Rozsa, Zoltan Madaras, Akos Sziranyi, Tamas HUN REN Inst Comp Sci & Control HUN REN SZTAK Machine Percept Res Lab H-1111 Budapest Hungary Budapest Univ Technol & Econ Fac Transportat Engn & Vehicle Engn Dept Mat Handling & Logist Syst H-1111 Budapest Hungary

LiDAR point clouds are a rich source of information for autonomous vehicles and ADAS systems. However, they can be challenging to segment for moving objects as - among other things - finding correspondences between sparse point clouds of consecutive frames is difficult. Traditional methods rely on a (global or local) map of the environment, which can be demanding to acquire and maintain in real-world conditions and the presence of the moving objects themselves. This paper proposes a novel approach using as minimal sweeps as possible to decrease the computational burden and achieve mapless moving object segmentation (MOS) in LiDAR point clouds. Our approach is based on a multimodal learning model with single-modal inference. The model is trained on a dataset of LiDAR point clouds and related camera images. The model learns to associate features from the two modalities, allowing it to predict dynamic objects even in the absence of a map and the camera modality. We propose semantic information usage for multi-frame instance segmentation in order to enhance performance measures. We evaluate our approach to the SemanticKITTI and Apollo real-world autonomous driving datasets. Our results show that our approach can achieve state-of-the-art performance on moving object segmentation and utilize only a few (even one) LiDAR frames.

关键词： Laser radar Point cloud compression object segmentation Cameras Autonomous vehicles Training Proposals Knowledge transfer Vehicle dynamics Semantics LiDAR point clouds moving object segmentation knowledge transfer autonomous driving

来源：评论

学校读者我要写书评

暂无评论

DeepMHT: moving object segmentation in the Point Cloud Based on Deep Learning and Multiple Hypothesis Tracking 5th

DeepMHT: Moving Object Segmentation in the Point Cloud Based...

引用

5th China Intelligent Robotics Academic Conference

作者： Chen, Baifan Jin, Yuxuan Zhao, Zishuo Wan, Jiadong Peng, Chengyu Cent South Univ Sch Automat Changsha 410073 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

In autonomous driving, moving object segmentation plays an essential role in improving the accuracy of mapping and path planning. However, accurately and consistently differentiating between objects that are actually moving and those that are potentially moving is still challenging. This paper proposes DeepMHT, a novel approach that combines a multiple hypothesis tracking algorithm (MHT) with deep learning to solve the issue of moving object segmentation of LiDAR point clouds. This paper designs a data association network to extract deep features from the object point cloud and 3D box and fuses these two features to obtain affinity weights. We propose an innovative local hypothesis update model that integrates affinity weights with the weights of MHT to make data associations more accurate and stable. Unlike other moving object segmentation algorithms that calculate differences in the point clouds of adjacent frames, DeepMHT directly estimates the motion state of objects and segments moving objects by their position and velocity, which significantly improves the precision of segmentation. This paper compares DeepMHT with several other moving object segmentation algorithms on the KITTI tracking dataset and finds that DeepMHT is highly accurate.

关键词： moving object segmentation learning-based data association multiple hypothesis tracking

来源：评论

学校读者我要写书评

暂无评论

moving object segmentation in Daubechies complex wavelet domain

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2015年第3期9卷 635-650页

作者： Khare, Manish Srivastava, Rajneesh Kumar Khare, Ashish Univ Allahabad Dept Elect & Commun Allahabad 211002 Uttar Pradesh India

Motion segmentation is a crucial step in video analysis and is associated with a number of computer vision applications. This paper introduces a new method for segmentation of moving object which is based on double change detection technique applied on Daubechies complex wavelet coefficients of three consecutive frames. Daubechies complex wavelet transform for segmentation of moving object has been chosen as it is approximate shift invariant and has a better directional selectivity as compared to real valued wavelet transform. Double change detection technique is used to obtain video object plane by inter-frame difference of three consecutive frames. Double change detection technique also provides automatic detection of appearance of new objects. The proposed method does not require any other parameter except Daubechies complex wavelet coefficients. Results of the proposed method for segmentation of moving objects are compared with results of other state-of-the-art methods in terms of visual performance and a number of quantitative performance metrics viz. Misclassification Penalty, Relative Foreground Area Measure, Pixel Classification Based Measure, Normalized Absolute Error, and Percentage of Correct Classification. The proposed method is found to have high degree of segmentation accuracy than the other state-of-the-art methods.

关键词： Double change detection Daubechies complex wavelet transform moving object segmentation

来源：评论

学校读者我要写书评

暂无评论

moving object segmentation in the H.264 compressed domain

引用

OPTICAL ENGINEERING 2007年第1期46卷 017003-1-017003页

作者： Liu, Zhi Zhang, Zhaoyang Shen, Liquan Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China Shanghai Univ Key Lab Adv Display & Syst Applicat Minist Educ Shanghai 200072 Peoples R China

An efficient approach is proposed for moving object segmentation in the H. 264 compressed domain. The normalized motion vector field is derived from the H. 264 compressed video and is exploited to segment the frame into motion-homogenous regions. The following region merging process is efficiently represented using a binary partition tree (BPT). Selective nodes are extracted from the traversal process of BPT to represent different moving objects. Experimental results demonstrate the good segmentation performance of our approach. In fact, the work presents a general BPT-based compressed-domain moving object segmentation approach, which can be easily adapted to other compressed domains including the moving Picture Expert Group (MPEG) domain. (c) 2007 Society of Photo-Optical Instrumentation Engineers.

关键词： moving object segmentation H.264 binary partition tree motion vector field region merging

来源：评论

学校读者我要写书评

暂无评论

moving object segmentation in DCT-based compressed video

引用

ELECTRONICS LETTERS 2000年第21期36卷 1769-1770页

作者： Ji, S Park, HW Korea Adv Inst Sci & Technol Dept Elect Engn Yusong Gu Taejon 305701 South Korea

A block-based automatic segmentation algorithm has been developed for detecting and tracking moving objects in DCT-based compressed video. The proposed algorithm segments moving objects with block resolution using the... 详细信息

关键词： moving object segmentation block resolution Image and video coding Integral transforms in numerical analysis data compression image segmentation stochastic behaviour image blocks Video signal processing DCT-based compressed video object recognition discrete cosine transforms video coding block-based automatic segmentation algorithm Image recognition

来源：评论

学校读者我要写书评

暂无评论

moving object segmentation by background subtraction and temporal analysis

引用

IMAGE AND VISION COMPUTING 2006年第5期24卷 411-423页

作者： Spagnolo, P. Orazio, T. D' Leo, M. Distante, A. CNR Ist Studi Sistemi Intelligent Automaz I-70126 Bari Italy

In this paper, we address the problem of moving object segmentation using background subtraction. Solving this problem is very important for many applications: visual surveillance of both in outdoor and indoor environments, traffic control, behavior detection during sport activities, and so on. All these applications require as a first step, the detection of moving objects in the observed scene before applying any further technique for object recognition and activity identification. We propose a reliable foreground segmentation algorithm that combines temporal image analysis with a reference background image. We are especially careful of the core problem arising in the analysis of outdoor daylight scenes: continuous variations of lighting conditions that cause unexpected changes in intensities on the background reference image. In this paper, a new approach for background adaptation to changes in illumination is presented. All the pixels in the image, even those covered by foreground objects, are continuously updated in the background model. The experimental results demonstrate the effectiveness of the proposed algorithm when applied in different outdoor and indoor environments. (C) 2006 Elsevier B.V. All rights reserved.

关键词： moving object segmentation temporal analysis background updating

来源：评论

学校读者我要写书评

暂无评论

moving object segmentation Network for Multiview Fusion of Vehicle-Mounted LiDAR

引用

IEEE SENSORS JOURNAL 2024年第21期24卷 36204-36215页

作者： Gan, Jianwang Zhang, Guoying Xiong, Yijin Gan, Yongqi Univ Min & Technol Beijing Sch Artificial Intelligence Beijing 100083 Peoples R China

Accurate motion recognition of objects in the surrounding environment using LiDAR sensors is crucial for the safe operation of driverless vehicles. Recent studies have extensively utilized 3-D data or 2-D projections to characterize LiDAR point cloud sequences for efficient moving target recognition. However, these approaches still face challenges in accurately identifying slow-moving or small-sized moving objects. To address these limitations, we propose a moving target segmentation network. Our network comprises three key components: the point view branches that preserve complete information, the bird's eye view (BEV) branches dedicated to motion feature construction, and the range view (RV) branches that compensate for semantic features. In the initial segment of the BEV branch, we introduce a differential attention-based dynamic information enhancement module (DIEM). DIME effectively enhances the dynamic information of both slow and small moving targets by integrating dynamic features between the current frame and its neighboring frames. During the BEV and RV encoding stage, we employ residual blocks to capture semantic features effectively. To expedite global feature capture, we propose a U-shaped pyramid pooling module (U-PPM). In the decoder, we present a multiscale dynamic information calibration module (MDICM) designed to calibrate and fuse features from different layers. Finally, the BEV and RV branch output features are back-projected into the 3-D space and fused with the point features to achieve motion target segmentation. Our method is validated on the public datasets of SemanticKITTI and Apollo, achieving advanced accuracy while meeting real-time requirements.

关键词： 2-D projection dynamic information calibration dynamic information enhancement (DIEM) moving object segmentation point cloud

来源：评论

学校读者我要写书评

暂无评论

moving object segmentation and detection for monocular robot based on active contour model

引用

ELECTRONICS LETTERS 2005年第24期41卷 1320-1322页

作者： Liu, PR Meng, MQH Liu, PX Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

A novel geodesic active contour model based on optical flow information is proposed to segment and detect the moving object for monocular robots. More specifically, an active contour is formulated using the level set method which eliminates the need of re-initialisation. The developed scheme alleviates the effect of optical flow noise, increasing the robustness of the detection of moving objects. Experimental results show that this algorithm can successfully track a moving target, e.g. a human being.

关键词： active vision image segmentation image sequences object detection robotvision tracking geodesic active contour model image segmentation image sequences level setmethod monocular robot moving object detection moving object segmentation optical flow info

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：