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Joint Stereo Video Deblurring, Scene Flow Estimation and moving object segmentation

IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 1748-1761页

作者： Pan, Liyuan Dai, Yuchao Liu, Miaomiao Porikli, Fatih Pan, Quan Australian Natl Univ Res Sch Engn Canberra ACT 2601 Australia Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Shaanxi Peoples R China Northwestern Polytech Univ Sch Automat Xian 710129 Shaanxi Peoples R China

Stereo videos for the dynamic scenes often show unpleasant blurred effects due to the camera motion and the multiple moving objects with large depth variations. Given consecutive blurred stereo video frames, we aim to recover the latent clean images, estimate the 3D scene flow and segment the multiple moving objects. These three tasks have been previously addressed separately, which fail to exploit the internal connections among these tasks and cannot achieve optimality. In this paper, we propose to jointly solve these three tasks in a unified framework by exploiting their intrinsic connections. To this end, we represent the dynamic scenes with the piece-wise planar model, which exploits the local structure of the scene and expresses various dynamic scenes. Under our model, these three tasks are naturally connected and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes). By exploiting the blur model constraint, the moving objects and the 3D scene structure, we reach an energy minimization formulation for joint deblurring, scene flow and segmentation. We evaluate our approach extensively on both synthetic datasets and publicly available real datasets with fast-moving objects, camera motion, uncontrolled lighting conditions and shadows. Experimental results demonstrate that our method can achieve significant improvement in stereo video deblurring, scene flow estimation and moving object segmentation, over state-of-the-art methods.

关键词： Estimation Cameras Motion segmentation object segmentation Image segmentation Kernel Semantics Stereo deblurring motion blur scene flow moving object segmentation joint optimization

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RVMOS: Range-View moving object segmentation Leveraged by Semantic and Motion Features

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第3期7卷 8044-8051页

作者： Kim, Jaeyeul Woo, Jungwan Im, Sunghoon DGIST Dept Elect Engn & Comp Sci Daegu 42988 South Korea

Detecting traffic participants is an essential and age-old problem in autonomous driving. Recently, the recognition of moving objects has emerged as a major issue in this field for safe driving. In this paper, we present RVMOS, a LiDAR Range-View-based moving object segmentation framework that segments moving objects given a sequence of range-view images. In contrast to the conventional method, our network incorporates both motion and semantic features, each of which encodes the motion of objects and the surrounding circumstance of the objects. In addition, we design a new feature extraction module suitably designed for range-view images. Lastly, we introduce simple yet effective data augmentation methods: time interval modulation and zero residual image synthesis. With these contributions, we achieve a 19% higher performance (mIoU) with 10% faster computational time (34 FPS on RTX 3090) than the state-of-the-art method with the SemanticKitti benchmark. Extensive experiments demonstrate the effectiveness of our network design and data augmentation scheme.

关键词： Autonomous driving LiDAR moving object segmentation perception range-view

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Single change detection-based moving object segmentation by using Daubechies complex wavelet transform

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IET IMAGE PROCESSING 2014年第6期8卷 334-344页

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

Research in motion analysis is a challenging field and it has a variety of video surveillance applications. For any video surveillance application, background detection and removal plays an important role in segmentation of the moving objects. This study proposes a new method for segmentation of the moving object, which is based on single change detection applied on Daubechies complex wavelet coefficients of two consecutive frames. The authors have chosen Daubechies complex wavelet transform as it is shift invariant and has a better directional selectivity as compared with real-valued wavelet transforms. Single change detection is a method to obtain video object plane by inter-frame difference of two consecutive frames, and it provides automatic detection of appearances of new objects. The proposed method does not require any other parameter except wavelet coefficients. segmentation results of the moving objects after applying the proposed method are compared with those obtained after applying other spatial and wavelet domain segmentation methods in terms of visual performance and a number of quantitative measures viz misclassification penalty, relative position-based measure, structural content, normalised absolute error and average difference and the proposed method is found better than the other methods.

关键词： image motion analysis image segmentation object detection video signal processing video surveillance wavelet transforms motion analysis video surveillance applications background detection moving object segmentation single change detection Daubechies complex wavelet coefficients Daubechies complex wavelet transform shift invariant directional selectivity real-valued wavelet transforms video object plane inter-frame difference automatic detection object appearances spatial domain segmentation wavelet domain segmentation visual performance quantitative measures misclassification penalty relative position-based measure structural content normalised absolute error

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Automatic moving object segmentation methods under varying illumination conditions for video data: comparative study, and an improved method

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第23期75卷 16209-16264页

作者： Kushwaha, Alok Kumar Singh Srivastava, Rajeev Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

In recent past, many moving object segmentation methods under varying lighting changes have been proposed in literature and each of them has their own benefits and limitations. The various methods available in literature for moving object segmentation may be broadly classified into four categories i.e., moving object segmentation methods based on (i) motion information (ii) motion and spatial information (iii) learning (iv) and change detection. The objective of this paper is two-fold i.e., firstly, this paper presents a comprehensive comparative study of various classical as well as state-of-the art methods for moving object segmentation under varying illumination conditions under each of the above mentioned four categories and secondly this paper presents an improved approximation filter based method in complex wavelet domain and its comparison with other methods under four categories mentioned as above. The proposed approach consist of seven steps applied on given video frames which include: wavelet decomposition of frames using Daubechies complex wavelet transform;use of improved approximate median filter on detail co-efficient (LH, HL, HH);use of background modeling on approximate co-efficient (LL sub-band);soft thresholding for noise removal;strong edge detection;inverse wavelet transformation for reconstruction;and finally using closing morphology operator. The qualitative and quantitative comparative study of the various methods under four categories as well as the proposed method is presented for six different datasets. The merits, demerits, and efficacy of each of the methods under consideration have been examined. The extensive experimental comparative analysis on six different challenging benchmark data sets demonstrate that proposed method is performing better to other state-of-the-art moving object segmentation methods and is well capable of dealing with various limitations of existing methods.

关键词： moving object segmentation Comparative study Performance analysis Wavelet domain

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Mean shift clustering-based moving object segmentation in the H.264 compressed domain

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IET IMAGE PROCESSING 2010年第1期4卷 11-18页

作者： Fei, W. Zhu, S. Marvell Technol Shanghai Ltd Shanghai 201203 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

This study presents a mean shift clustering-based moving object segmentation approach in the H. 264 compressed domain. The motion information extracted from H. 264 compressed video, including motion vectors (MVs) and partitioned block size, are used as segmentation cues. The MVs are processed by normalisation, weighted 3D median filter and motion compensation to obtain a reliable and salient MV field. The partitioned block size is used as a measure of motion texture in the process of the MV field. Based on the processed MV field, the authors employ the mean shift-based mode seeking in spatial, temporal and range domain to develop a new approach for compact representation of the MV field. Then, the MV field is segmented into different motion-homogenous regions by clustering the modes with small spatial and range distance, and each object is represented by some dominant modes. Experimental results for several H. 264 compressed video sequences demonstrate good performance and efficiency of the proposed segmentation approach.

关键词： median filters video coding motion information mean shift clustering H.264 compressed video Image and video coding motion compensation moving object segmentation Other topics in statistics partitioned block size weighted 3D median filter motion vector data compression estimation theory image segmentation Video signal processing

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A novel moving object segmentation framework utilizing camera motion recognition for H.264 compressed videos

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2016年 36卷 199-212页

作者： Okade, Manish Biswas, Prabir Kumar Natl Inst Technol Dept Elect & Commun Engn Rourkela 769008 India Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

This paper presents a novel coarse to fine moving object segmentation framework for H.264/AVC compressed videos. The proposed framework integrates the global motion estimation and global motion compensation steps in the segmentation pipeline unlike previous techniques which did not consider such an integration. The integration is based on testing for presence of global motion by classifying the interframe motion vectors into moving camera class and still camera class. The decision boundary separating these two classes is learnt from the training video data. The integration automates the moving object segmentation to be applicable for static, moving and combination of static/moving camera cases which to the best of our knowledge has not been carried out earlier. Further, a novel coarse segmentation technique is proposed by decomposing the inter-frame motion vectors into wavelet sub-bands and utilizing logical operations on LH, HL and HH sub-band wavelet coefficients. The premise is based on the fact that since the LH, HL and HH sub-bands contain the detail information pertaining to horizontal, vertical and diagonal moving blocks respectively, they can be exploited to identify the coarse moving boundaries. The coarse segmentation is fast in comparison to state-of-the-art coarse segmentation methods as demonstrated by our experiments. Finally, these coarse boundaries are modeled in an energy minimization framework and shown that by minimizing the energy using graph cut optimization the segmentation is refined to obtain the fine segmentation. The proposed framework is tested on a number of standard video sequences encoded with H.264/AVC JM encoder and comparison is carried out with state-of-the-art compressed domain moving object segmentation methods as well as with an existing state-of-the-art pixel domain method to establish and validate the proposed moving object segmentation framework. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Block motion vectors Camera motion moving object segmentation Wavelet subbands Neural network classifier Graph cut Compressed domain Back-propagation training

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Unsupervised evaluation method using Markov random field for moving object segmentation in infrared videos

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IET IMAGE PROCESSING 2014年第7期8卷 426-433页

作者： Min, Chaobo Zhang, Junju Chang, Bengkang Sun, Bin Li, Yingjie Nanjing Univ Sci & Technol Sch Elect Engn & Opt Elect Technol Nanjing 210094 Jiangsu Peoples R China

An unsupervised method is proposed for performance evaluation of the moving object segmentation using Markov random field (MRF) in infrared videos. This method focuses on the edge features and takes spatio-temporal information into account. The authors consider an MRF model for each edge point of a segmentation mask in spatial and temporal directions. This problem is then formulated using maximum a posteriori estimation principle to form a criterion of evaluation. Subjective evaluation is applied to measure the performance of the evaluation methods. The results show that the proposed method is superior to other unsupervised measures.

关键词： image motion analysis image segmentation Markov processes maximum likelihood estimation unsupervised learning video signal processing unsupervised evaluation method Markov random field moving object segmentation infrared videos performance evaluation edge features spatio-temporal information MRF model maximum a posteriori estimation principle subjective evaluation

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Pattern mining-based video saliency detection: Application to moving object segmentation

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 70卷 567-579页

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

In this paper, we present a new model for spatiotemporal saliency detection. Instead of previous works which combine the image saliency in the spatial domain with motion cues to build their video saliency model, we propose to apply the pattern mining (PM) algorithm. From initial saliency maps computed in spatial and temporal domains, discriminative spatiotemporal saliency patterns can be recognized and their label information is propagated to obtain the final saliency map. Our model ensures a good compromise between image saliency and motion saliency and presents an accurate prediction to estimate salient regions in comparison with other methods for video saliency detection. Finally, as an application of our algorithm, our spatiotemporal saliency map is combined with appearance models and dynamic location models into an energy minimization framework to segment salient moving object. Experiments show a good performance of our algorithm for moving object segmentation (MOS) on benchmark datasets. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Spatiotemporal saliency Pattern mining algorithm Spatiotemporal saliency patterns Image saliency Motion saliency moving object segmentation Energy minimization

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Improved moving object segmentation by multiresolution and variable thresholding

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OPTICAL ENGINEERING 2006年第11期45卷 117003-1-117003页

作者： Wu, Hsien-Huang P. Chang, Jen-Hung Weng, Ping-Kuo Wu, Ying-Yih Natl Yunlin Univ Sci & Technol Dept Elect Engn Yunlin 640 Douliou Taiwan Chung Shan Inst Sci & Technol Mat RD Ctr Solid State Devices Mat Sect Tao Yuan 325 Taiwan

segmentation of moving objects in image sequences by change detection has been a very important topic in multimedia and surveillance applications. One popular approach is to model the background of the scene and then threshold the differences between the background and the input images to detect the change caused by the moving object. Although this idea is simple and effective, the selection of a proper threshold value faces a trade-off between false alarms and misdetection. In this paper, a new method of thresholding for moving object segmentation in scenes without dynamic background and rapid variation of illumination is proposed to avoid misdetection while reducing false alarms. This new approach adopts the concept of thresholding with hysteresis, which utilizes a multiresolution and variable thresholding (MRVT) scheme to achieve improvements on the segmentation performance. Combined with a module of shadow removal, MRVT can generate accurate moving object masks. segmentation results are evaluated qualitatively and quantitatively for indoor scenes, and the effectiveness of MRVT is encouraging. Compared with two other state-of-the-art approaches, our proposed method can achieve more accurate object boundary, fewer false alarms, and reduced fragmentation of objects. (c) 2006 Society of Photo-Optical Instrumentation Engineers.

关键词： moving object segmentation adaptive thresholding thresholding with hysteresis multiresolution variable thresholding background modeling

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Optical flow estimation and moving object segmentation based on median radial basis function network

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1998年第5期7卷 693-702页

作者： Bors, AG Pitas, I Univ Thessaloniki Dept Informat GR-54006 Thessaloniki Greece

Various approaches have been proposed for simultaneous optical flow estimation and segmentation in image sequences, In this study, the moving scene is decomposed into different regions with respect to their motion, by means of a pattern recognition scheme, The inputs of the proposed scheme are the feature vectors representing still image and motion information. Each class corresponds to a moving object. The classifier employed is the median radial basis function (MRBF) neural network. An error criterion function derived from the probability estimation theory and expressed as a function of the moving scene model is used as the cost function. Each basis function is activated by a certain image region. Marginal median and median of the absolute deviations from the median (MAD) estimators are employed for estimating the basis function parameters. The image regions associated with the basis functions are merged by the output units in order to identify moving objects.

关键词： energy minimization median radial basis function neural network moving object segmentation optical flow estimation robust training

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