Zhifei Xu

Bio: Zhifei Xu is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Object detection & Background subtraction. The author has an hindex of 3, co-authored 3 publications receiving 34 citations.

An eigenbackground subtraction method using recursive error compensation

Abstract: Eigenbackground subtraction is a commonly used method for moving object detection. The method uses the difference between an input image and the reconstructed background image for detecting foreground objects based on eigenvalue decomposition. In the method, foreground regions are represented in the reconstructed image using eigenbackground in the sense of least mean squared error minimisation. This results in errors that are spread over the entire reconstructed reference image. This will also result in degradation of quality of reconstructed background leading to inaccurate moving object detection. In order to compensate these regions, an efficient method is proposed by using recursive error compensation and an adaptively computed threshold. Experiments were conducted on a range of image sequences with variety of complexity. Performance were evaluated both qualitatively and quantitatively. Comparisons made with two existing methods have shown better approximations of the background images and more accurate detection of foreground objects have been achieved by the proposed method.

Recursive error-compensated dynamic eigenbackground learning and adaptive background subtraction in video

Abstract: We address the problem of foreground object detection through background subtraction. Although eigenbackground models are successful in many computer vision applications, background subtraction methods based on a conventional eigenbackground method may suffer from high false-alarm rates in the foreground detection due to possible absorption of foreground changes into the eigenbackground model. This paper introduces an improved eigenbackground modeling method for videos by recursively applying an error compensation process to reduce the influence of foreground moving objects on the eigenbackground model. An adaptive threshold method is also introduced for background subtraction, where the threshold is determined by combining a fixed global threshold and a variable local threshold. A fast algorithm is then given as an approximation to the proposed method by imposing and exploiting a constraint on motion consistency, leading to about 50% reduction in computations. Experiments have been performed on a range of videos with satisfactory results. Performance is evaluated using an objective criterion. Comparisons are made with two existing methods.

Video segmentation using joint space-time-range adaptive mean shift

Abstract: Video segmentation has drawn increasing interest in multimedia applications This paper proposes a novel joint space-time-range domain adaptive mean shift filter for video segmentation In the proposed method, segmentation of moving/static objects/background is obtained through inter-frame mode-matching in consecutive frames and motion vector mode estimation Newly appearing objects/regions in the current frame due to new foreground objects or uncovered background regions are segmented by intra-frame mode estimation Simulations have been conducted to several image sequences, and results have shown the effectiveness and robustness of the proposed method Further study is continued to evaluate the results

Traditional and recent approaches in background modeling for foreground detection: An overview

Abstract: Background modeling for foreground detection is often used in different applications to model the background and then detect the moving objects in the scene like in video surveillance. The last decade witnessed very significant publications in this field. Furthermore, several surveys can be found in literature but none of them addresses an overall review in this field. So, the purpose of this paper is to provide a complete survey of the traditional and recent approaches. First, we categorize the different approaches found in literature. We have classified them in terms of the mathematical models used and we have discussed them in terms of the critical situations that they claim to handle. Furthermore, we present the available resources, datasets and libraries. Then, we conclude with several promising directions for future research.

Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey

Abstract: Background modeling is currently used to detect moving objects in video acquired from static cameras. Numerous statistical methods have been developed over the recent years. The aim of this paper is firstly to provide an extended and updated survey of the recent researches and patents which concern statistical background modeling and secondly to achieve a comparative evaluation. For this, we firstly classified the statistical methods in terms of category. Then, the original methods are reminded and discussed following the challenges met in video sequences. We classified their respective improvements in terms of strategies used. Furthermore, we discussed them in terms of the critical situations they claim to handle. Finally, we conclude with several promising directions for future research. The survey also discussed relevant patents.

Statistical Background Modeling for Foreground Detection: A Survey

Abstract: Background modeling is often used in the context of moving objects detection from static cameras. Numerous methods have been developed over the recent years and the most used are the statistical ones. The purpose of this chapter is to provide a recent survey of these different statistical methods. For this, we have classified them in term of generation following the years of publication and the statistical tools used. We then focus on the first generation methods: Single Gaussian, Mixture of Gaussians, Kernel Density Estimation and Subspace Learning using PCA. These original methods are reminded and then we have classified their different improvements in term of strategies. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.

Subspace Learning for Background Modeling: A Survey

Abstract: Background modeling is often used to detect moving object in video acquired by a fixed camera. Recently, subspace learning methods have been used to model the background in the idea to represent online data content while reducing dimension significantly. The first method using Principal Component Analysis (PCA) was proposed by Oliver et al. and a representative patent using PCA concerns the detection of cars and persons in video surveillance. Numerous improvements and variants were developed over the recent years. The purpose of this paper is to provide a survey and an original classification of these improvements. Firstly, we classify the improvements of the PCA in term of strategies and the variants in term of the used subspace learning algorithms. Then, we present a comparative evaluation of the variants and evaluate them with the state-of-art algorithms (SG, MOG, and KDE) by using the Wallflower dataset.

A novel video dataset for change detection benchmarking.

Abstract: Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries-an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website 1 and will be updated with feedback from academia and industry in the future.