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moving object detection based on frame difference and W4

SIGNAL IMAGE AND VIDEO PROCESSING 2017年第7期11卷 1357-1364页

作者： Sengar, Sandeep Singh Mukhopadhyay, Susanta Indian Sch Mines Indian Inst Technol Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

moving object detection is a basic and important task on automated video surveillance systems, because it gives the focus of attention for further examination. Frame differencing and W4 algorithm can be individually employed to detect the moving objects. However, the detected results of the individual approach are not accurate due to foreground aperture and ghosting problems. We propose an approach to segment the moving objects using both the frame differencing and W4 algorithm to overcome the above problems. Here first we compute the difference between consecutive frames using histogram-based frame differencing technique, next W4 algorithm is applied on frame sequences, and subsequently, the outcomes of the frame differencing and W4 algorithm are combined using logical 'OR' operation. Finally, morphological operation with connected component labeling is employed to detect the moving objects. The experimental results and performance evaluation on real video datasets demonstrate the effectiveness of our approach in comparison with existing techniques.

关键词： moving object detection W4 Frame differencing Morphology Histogram

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moving object detection using modified temporal differencing and local fuzzy thresholding

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JOURNAL OF SUPERCOMPUTING 2017年第3期73卷 1120-1139页

作者： Paul, Nihal Singh, Ashish Midya, Abhishek Roy, Partha Pratim Dogra, Debi Prosad Natl Inst Technol Silchar Dept Elect & Commun Engn Silchar India Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar Orissa India

Most of the existing video object detection schemes are either computationally extensive or fail to detect moving objects in different challenging situations. In this paper, we propose a robust and computationally inexpensive scheme to detect moving objects in video. The threefold approach begins with computation of difference images using temporal information. Difference images are calculated by subtracting two input frames, at each pixel position. Instead of generating difference images using the traditional continuous frame difference approach, we propose using a fixed number of alternate frames centered around the current frame. This approach aids in reducing the computational complexity without compromising on quality of the difference images. After computation of difference images, a novel post-processing scheme is employed by utilizing gamma correction factor and Mahalanobis distance metric to reduce false positives and false negatives. object segmentation is finally performed on the refined difference image by a local fuzzy thresholding scheme. This avoids problems that are usually encountered in hard thresholding, especially pixel misclassification, which is the most important one. For robust experimental analysis, videos from ***, CAVIAR, and http://perception.i2r datasets have been used. These selected videos contain a wide variety of common challenges faced during object detection. Some examples are the presence of dynamic backgrounds, shadows, bad weather, etc. The results establish the effectiveness of the proposed scheme over some of the existing schemes both qualitatively and quantitatively as delineated in the experimental result section.

关键词： moving object detection Temporal information Fuzzy thresholding Dynamic background Difference image Ghosting

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moving object detection and segmentation in urban environments from a moving platform

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IMAGE AND VISION COMPUTING 2017年 68卷 76-87页

作者： Zhou, Dingfu Fremont, Vincent Quost, Benjamin Dai, Yuchao Li, Hongdong Univ Technol Compiegne Sorbonne Univ CNRS UMR 7253Heudiasyc CS 60319 F-60203 Compiegne France Australian Natl Univ Res Sch Engn Canberra ACT Australia ARC Ctr Excellence Robot Vis Brisbane Qld Australia

This paper proposes an effective approach to detect and segment moving objects from two time-consecutive stereo frames, which leverages the uncertainties in camera motion estimation and in disparity computation. First, the relative camera motion and its uncertainty are computed by tracking and matching sparse features in four images. Then, the motion likelihood at each pixel is estimated by taking into account the ego-motion uncertainty and disparity in computation procedure. Finally, the motion likelihood, color and depth cues are combined in the graph-cut framework for moving object segmentation. The efficiency of the proposed method is evaluated on the KITTI benchmarking datasets, and our experiments show that the proposed approach is robust against both global (camera motion) and local (optical flow) noise. Moreover, the approach is dense as it applies to all pixels in an image, and even partially occluded moving objects can be detected successfully. Without dedicated tracking strategy, our approach achieves high recall and comparable precision on the KITTI benchmarking sequences. (C) 2017 Elsevier B.V. All rights reserved.

关键词： moving object detection Ego-motion uncertainty Motion segmentation

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Distribution majorization of Corner Points by Reinforcement Learning for moving object detection 10

Distribution majorization of Corner Points by Reinforcement ...

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10th International Conference on Machine Vision (ICMV)

作者： Wu Hao Yu Hao Zhou Dongxiang Cheng Yongqiang Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (数字)9781510619425

ISBN: (纸本)9781510619425

Corner points play an important role in moving object detection, especially in the case of free-moving camera. Corner points provide more accurate information than other pixels and reduce the computation which is unnecessary. Previous works only use intensity information to locate the corner points, however, the information that former and the last frames provided also can be used. We utilize the information to focus on more valuable area and ignore the invaluable area. The proposed algorithm is based on reinforcement learning, which regards the detection of corner points as a Markov process. In the Markov model, the video to be detected is regarded as environment, the selections of blocks for one corner point are regarded as actions and the performance of detection is regarded as state. Corner points are assigned to be the blocks which are seperated from original whole image. Experimentally, we select a conventional method which uses marching and Random Sample Consensus algorithm to obtain objects as the main framework and utilize our algorithm to improve the result. The comparison between the conventional method and the same one with our algorithm show that our algorithm reduce 70% of the false detection.

关键词： moving object detection corner points reinforcement learning k-armed bandit

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Tensor Total Variation Regularized moving object detection for Surveillance Videos 12

Tensor Total Variation Regularized Moving Object Detection f...

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12th International Conference on Signal Processing and Communications (SPCOM)

作者： Tom, Anju Jose George, Sudhish N. Natl Inst Technol Dept Elect & Commun Engn Calicut 673601 Kerala India

ISBN: (纸本)9781538638217

The classical Background Subtraction (BS) and moving object detection (MOD) problems function on the matrix framework considering each frame as a matrix. This work proposes a method in which the video data is treated as a tensor throughout the implementation and thereby ensuring efficient utilization of the structural properties of the video volume. It also addresses the dynamic background issue (swaying trees, moving water, waves etc.) by solving a tensor optimization algorithm of a convex formulation that is convergent in nature. Moreover, the low-rank property is used to extract the structured part of the scene while Tensor Total Variation (TTV) is incorporated to draw out the foreground part of the emotive surroundings. The excellence of this method lies in the reduced execution time and on the superiority acquired in quantitative evaluation based on F-measure, Recall, and Precision with respect to the state of the art methods.

关键词： moving object detection Low-Rank Recovery Background Subtraction Tensor Total Variation

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moving object detection Using Tensor-Based Low-Rank and Saliently Fused-Sparse Decomposition

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2017年第2期26卷 724-737页

作者： Hu, Wenrui Yang, Yehui Zhang, Wensheng Xie, Yuan Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

In this paper, we propose a new low-rank and sparse representation model for moving object detection. The model preserves the natural space-time structure of video sequences by representing them as three-way tensors. Then, it operates the low-rank background and sparse foreground decomposition in the tensor framework. On the one hand, we use the tensor nuclear norm to exploit the spatio-temporal redundancy of background based on the circulant algebra. On the other, we use the new designed saliently fused-sparse regularizer (SFS) to adaptively constrain the foreground with spatio-temporal smoothness. To refine the existing foreground smooth regularizers, the SFS incorporates the local spatio-temporal geometric structure information into the tensor total variation by using the 3D locally adaptive regression kernel (3D-LARK). What is more, the SFS further uses the 3D-LARK to compute the space-time motion saliency of foreground, which is combined with the l(1) norm and improves the robustness of foreground extraction. Finally, we solve the proposed model with globally optimal guarantee. Extensive experiments on challenging well-known data sets demonstrate that our method significantly outperforms the state-of-the-art approaches and works effectively on a wide range of complex scenarios.

关键词： moving object detection tensor nuclear norm tensor total variation space-time visual saliency

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Online Illumination Invariant moving object detection by Generative Neural Network 11

Online Illumination Invariant Moving Object Detection by Gen...

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11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP)

作者： Bahri, Fateme Shakeri, Moein Ray, Nilanjan Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (纸本)9781450366151

moving object detection (MOD) is a significant problem in computer vision that has many real world applications. Different categories of methods have been proposed to solve MOD. Challenges include separating moving objects from illumination changes and shadows in real world videos. State-of-the-art methods that can handle illumination changes and shadows work in a batch mode;thus, these methods are not suitable for long video sequences or real-time applications. In this paper, we propose an extension of a state-of-the-art batch MOD method (ILISD) [23] to an online/incremental MOD using unsupervised and generative neural networks, which use illumination invariant image representations. For each image in a sequence, we use a low-dimensional representation of a background image by a neural network and then based on the illumination invariant representation, decompose the foreground image into illumination change and moving objects. Optimization is performed by stochastic gradient descent in an end-to-end and unsupervised fashion. Our algorithm can work in both batch and online modes. In the batch mode, like other batch methods, optimizer uses all the images. In the online mode, images are incrementally fed into the optimizer. Based on our experimental evaluation on benchmark image sequences, both the online and the batch modes of our algorithm achieve state-of-the-art accuracy on many data sets.

关键词： moving object detection illumination invariance Generative neural network online background modeling

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A Feature Pair Based Method for Online moving object detection from High-Resolution Airborne Videos

A Feature Pair Based Method for Online Moving Object Detecti...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Xiaosong Lan Chengfei Zhu Shuxiao Li Hongxing Chang Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728123264

Current researches on moving object detection from airborne videos mainly focus on low-resolution videos. Recently, to achieve a wide-area of fine-grained surveillance, most of unmanned aerial vehicles (UAV) prefer capturing high-resolution videos. Traditional methods usually require the processing of many neighboring frames to achieve a good performance for detecting moving objects in a frame. However, for high-resolution videos, due to limited computing resources, it is difficult to process many frames online, making traditional methods unsuitable for this task. In this paper, we propose a feature pair based method for moving object detection from high-resolution videos. It only needs two frames to detect moving objects and most of operations are performed in the feature pair domain with high efficiency, being well suited for high-resolution situation. Moreover, we design four strategies, i.e., feature pair extraction, matched pair refinement, moving pair determination, and clustering strategies, to enable an accurate detection of moving objects. Experimental results demonstrate an excellent performance of the proposed method for moving object detection from high-resolution airborne videos.

关键词： moving object detection High-resolution Airborne videos Unmanned aerial vehicles Feature pair based Online moving target detection Videotapes High definition Unmanned Aerial Vehicle drone honey bees vehicle Pairing

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moving object detection With a Freely moving Camera via Background Motion Subtraction

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2017年第2期27卷 236-248页

作者： Wu, Yuanyuan He, Xiaohai Nguyen, Truong Q. Sichuan Univ Coll Elect & Informat Engn Chengdu 610064 Peoples R China Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA

detection of moving objects in a video captured by a freely moving camera is a challenging problem in computer vision. Most existing methods often assume that the background (BG) can be approximated by dominant single plane/multiple planes or impose significant geometric constraints on BG, or utilize a complex BG/foreground probabilistic model. Instead, we propose a computationally efficient algorithm that is able to detect moving objects accurately and robustly in a general 3D scene. This problem is formulated as a coarse-to-fine thresholding scheme on the particle trajectories in the video sequence. First, a coarse foreground (CFG) region is extracted by performing reduced singular value decomposition on multiple matrices that are built from bundles of particle trajectories. Next, the BG motion of pixels in the CFG region is reconstructed by a fast inpainting method. After subtracting the BG motion, the fine foreground is segmented out by an adaptive thresholding method that is capable of solving multiple-moving- objects scenarios. Finally, the detected foreground is further refined by the mean-shift segmentation method. Extensive simulations and a comparison with the state-of-the-art methods verify the effectiveness of the proposed method.

关键词： moving camera moving object detection particle trajectories thresholding

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moving object detection using median-based scale invariant local ternary pattern for video surveillance system

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第3期33卷 1933-1943页

作者： Kalirajan, K. Sudha, M. SVS Coll Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India Hindusthan Coll Engn & Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India

This article presents a novel moving object detection algorithm using median-based scale invariant local ternary pattern for intelligent video surveillance system. Both the texture and color local features are extracted from the incoming frames independently and they are combined at the classification level to improve the object detection results. Here, each incoming image frames are subdivided into several regions and the median-based scale invariant local ternary pattern (MD-SILTP) is obtained for each sub-region. Based on the MD-SILTP patterns, the texture histograms are computed and matched with the background model using the histogram intersection method. Furthermore, the color features are extracted through color histogram matching technique. The background model is then updated based on the best matching texture and color histograms. Finally, the color and texture information are combined for final feature classification. Experiment results illustrate that the fusion of MD-SILTP texture with the color features is stable than the others under smooth surface regions, image noises due to illumination changes, moving cast shadow, and scaling problems.

关键词： moving object detection median-SILTP pattern color histogram video surveillance background modelling

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