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IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Rahmon, Gani Bunyak, Filiz Seetharaman, Guna Palaniappan, Kannappan Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA US Naval Res Lab Washington DC USA

ISBN: (纸本)9781728182438

A fundamental processing step in a wide variety of video processing pipelines is detection of moving objects. moving object detection is a challenging task due to various environmental conditions such as illumination changes, shadows, glare, background clutter;foreground complexities such as occlusion, camouflage, complex motion behavior of the foreground objects;and imaging conditions such as low resolution and/or frame rate, camera jitter etc. While many moving object detection methods have been proposed, individual approaches often fail to address all the challenges efficiently. In this paper, we propose and evaluate different decision fusion mechanisms for robust detection of moving objects. The proposed hybrid system relies on motion, change, appearance cues and utilizes a mix of classical unsupervised computer vision and supervised deep learning approaches. Motion and change cues are computed through a tensor based motion estimation and a multi-modal background subtraction modules. Appearance cue is estimated using an unsupervised deep semantic segmentation network. These complementary visual cues are fused to achieve robust performance under challenging real-world conditions. The proposed multicue decision fusion pipelines were tested and evaluated on the CDnet-2014 change detection dataset. Our novel, unsupervised, expert-guided, semantic rule-based fusion approach with global channel filtering (SR-Fusion+GCF) is shown to outperform the other decision fusion strategies with an F-Measure of 69%, a promising 12% increase in F-measure compared to the best single cue approach.

关键词： moving object detection convolutional neural networks fusion

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moving object detection Using Integrated Spatial and Motion-Based Method 16

Moving Object Detection Using Integrated Spatial and Motion-...

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16th International Joint Conference on Computer Science and Software Engineering (JCSSE) - Knowledge Evolution Towards Singularity of Man-Machine Intelligence

作者： Chansuparp, Manit Jitkajornwanich, Kulsawasd King Mongkuts Inst Technol Ladkrabang Dept Comp Sci Fac Sci Bangkok 10520 Thailand

ISBN: (纸本)9781728107196

moving object detection is one of the key tasks in image processing due to its important role in many applications nowadays. In spite of the fact that many relevant active researches exist, these methods still have drawbacks. This paper proposes an integrated method to detect moving object in video sequence frames. To make the detection more robust, we adopt two well-known methods namely, optical flow and active contour, and added a new adaptive spatial thresholding. The optical flow is first applied on two sequence frames to differentiate the shift of flow vectors of these two. Then, the lost areas are retrieved by our adaptive spatial thresholding. Depending upon the intensity and spatial correlation of individual pixel, they can improve the accuracy and robustness of segmentation process. In the final step, active contour converges to unite moving areas into one. The experimental results show that our method outperforms the previous motion-based methods, especially in term of processing time.

关键词： moving object detection Optical flow Active contour Motion-based Spatial-based Adaptive Spatial thresholding

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Improvement of moving object detection Accuracy on Aerial Imagery Using Sensor Geometry 29

Improvement of Moving Object Detection Accuracy on Aerial Im...

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX

作者： Yoon, Deukryeol Heo, Wonyoung Kim, Seongjo Sung, Hyunseong Jeong, Jongmin Agcy Def Dev Daejeon South Korea

ISBN: (纸本)9781510636248;9781510636231

In this paper, we propose an efficient approach to improve moving object detection accuracy which is adaptive to changes in object size according to the altitude of the aircraft. The proposed algorithm can effectively detect moving objects with various pixel-scale from 8x8 to 100x100 in full-HD motion imagery. It consists of two stages which are detection and fusion. At the first stage, two algorithms are performed simultaneously: One is one-stage object detection network for detecting large objects, and the other is optical flow method for detecting small moving objects. In the second stage, results from the first stage are fused with a Ground Sample Distance (GSD) of imagery. We have conducted experiments using aerial imagery taken at a height between 130 meters and 400 meters. We evaluated the detection performance of our method in terms of precision, recall and normalized multiple object detection accuracy (N-MODA). Through experiments, we proved that the superiority of the proposed method.

关键词： moving object detection aerial imagery information fusion

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moving object detection under a moving Camera via Background Orientation Reconstruction

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SENSORS 2020年第11期20卷 3103.-3103.页

作者： Zhang, Wenlong Sun, Xiaoliang Yu, Qifeng Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China

moving object detection under a moving camera is a challenging question, especially in a complex background. This paper proposes a background orientation field reconstruction method based on Poisson fusion for detecting moving objects under a moving camera. As enlightening by the optical flow orientation of a background is not dependent on the scene depth, this paper reconstructs the background orientation through Poisson fusion based on the modified gradient. Then, the motion saliency map is calculated by the difference between the original and the reconstructed orientation field. Based on the similarity in appearance and motion, the paper also proposes a weighted accumulation enhancement method. It can highlight the motion saliency of the moving objects and improve the consistency within the object and background region simultaneously. Furthermore, the proposed method incorporates the motion continuity to reject the false positives. The experimental results obtained by employing publicly available datasets indicate that the proposed method can achieve excellent performance compared with current state-of-the-art methods.

关键词： moving object detection orientation field background reconstruction Poisson fusion motion saliency

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GPU-Accelerated Tensor Decomposition for moving object detection from Multimodal Imaging

GPU-Accelerated Tensor Decomposition for Moving Object Detec...

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IEEE Sensors Conference

作者： Bin, Junchi Kang, Meng Liu, Zheng Univ British Columbia Sch Engn Okanagan Campus Kelowna BC V1V 1V7 Canada

ISBN: (纸本)9781728168012

moving object detection is a crucial component of video surveillance. With advances in sensory technologies, a multimodal image sensor can collect various signals such as infrared signals and depth signals for accurate moving object detection. Recent approaches regard these signals as a single tensor and apply tensor decomposition for classifying moving objects. However, the size of tensor is usually large from the multimodal image sensor, which makes the decomposition extremely slow in practice. In this paper, a parallel framework is proposed to take advantage of the graphics processing unit (GPU) for accelerating tensor decomposition. Compared with the original tensor decomposition in the central processing unit (CPU), our best GPU implementation achieves up to 380% speedup in moving object detection on public data sets.

关键词： GPU acceleration tensor decomposition multimodal image sensors moving object detection

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A Super-Fast Deep Network for moving object detection 52

A Super-Fast Deep Network for Moving Object Detection

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hou, Bingxin Liu, Ying Ling, Nam Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA

ISBN: (纸本)9781728133201

Deep learning methods have been actively applied to intelligent video surveillance for moving object detection in recent years and demonstrated impressive results. However, these models render superior accuracy at the cost of high computational complexity. In this work, we devised a new deep network structure that significantly improves inference speed, yet requires 10 times smaller model size and achieves 10 times reduction in floating-point operations as compared to existing deep learning models with tolerable accuracy loss.

关键词： moving object detection video analytics background subtraction convolution neural network deep learning video surveillance

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moving object detection USING SPATIAL CORRELATION IN LAB COLOUR SPACE 3

MOVING OBJECT DETECTION USING SPATIAL CORRELATION IN LAB COL...

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International Workshop on Photogrammetric and Computer Vision Techniques for Video Surveillance, Biometrics and Biomedicine

作者： Roshan, A. Zhang, Y. Univ New Brunswick Fredericton Dept Geodesy & Geomat Engn Fredericton NB Canada

Background subtraction-based techniques of moving object detection are very common in computer vision programs. Each technique of background subtraction employs image thresholding algorithms. Different thresholding methods generate varying threshold values that provide dissimilar moving object detection results. A majority of background subtraction techniques use grey images which reduce the computational cost but statistics-based image thresholding methods do not consider the spatial distribution of pixels. In this study, authors have developed a background subtraction technique using Lab colour space and used spatial correlations for image thresholding. Four thresholding methods using spatial correlation are developed by computing the difference between opposite colour pairs of background and foreground frames. Out of 9 indoor and outdoor scenes, the object is detected successfully in 7 scenes whereas existing background subtraction technique using grey images with commonly used thresholding methods detected moving objects in 1-5 scenes. Shape and boundaries of detected objects are also better defined using the developed technique.

关键词： moving object detection Background Subtraction Lab Colour Space Spatial Correlation

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Deep learning based moving object detection for oblique images without future frames 30

Deep learning based moving object detection for oblique imag...

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Conference on Automatic Target Recognition XXX

作者： Heo, Won Yeong Kim, Seongjo Yoon, DeukRyeol Jeong, Jongmin Sung, HyunSeong Agcy Def Dev Daejeon South Korea

ISBN: (数字)9781510635661

ISBN: (纸本)9781510635661;9781510635654

moving object detection from UAV/aerial images is one of the essential tasks in surveillance systems. However, most of the works did not take account of the characteristics of oblique images. Also, many methods use future frames to detect moving objects in the current frame, which causes delayed detection. In this paper, we propose a deep learning based moving object detection method for oblique images without using future frames. Our network has a CNN (Convolutional Neural Network) architecture with the first and second layer containing sublayers with different kernel sizes. These sublayers play a role in detecting objects with different sizes or speeds, which is very important because objects that are closer to the camera look bigger and faster in oblique images. Our network takes the past five frames registered with respect to the last frame and produces a heatmap prediction for moving objects. Finally, we process a threshold processing to distinguish between object pixels and non-object pixels. We present the experimental results on our dataset. It contains about 15,000 images for training and about 6,000 images for testing with ground truth annotations for moving objects. We demonstrate that our method shows a better performance than some previous works.

关键词： military surveillance moving object detection oblique image deep learning

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A Robust moving object detection in Multi-Scenario Big Data for Video Surveillance

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2019年第4期29卷 982-995页

作者： Chen, Bo-Hao Shi, Ling-Feng Ke, Xiao Yuan Ze Univ Dept Comp Sci & Engn Taoyuan 320 Taiwan Yuan Ze Univ Innovat Ctr Big Data & Digital Convergence Taoyuan 320 Taiwan Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Fujian Peoples R China Fuzhou Univ Fujian Prov Key Lab Networking Comp & Intelligent Fuzhou 350116 Fujian Peoples R China

Advanced wireless imaging sensors and cloud data storage contribute to video surveillance by enabling the generation of large amounts of video footage every second. Consequently, surveillance videos have become one of the largest sources of unstructured data. Because multi-scenario surveillance videos are often continuously produced, using these videos to detect moving objects is challenging for the conventional moving object detection methods. This paper presents a novel model that harnesses both sparsity and low-rankness with contextual regularization to detect moving objects in multi-scenario surveillance data. In the proposed model, we consider moving objects as a contiguous outlier detection problem through the use of low-rank constraint with contextual regularization, and we construct dedicated backgrounds for multiple scenarios using dictionary learning-based sparse representation, which ensures that our model can be effectively applied to multi-scenario videos. Quantitative and qualitative assessments indicate that the proposed model outperforms existing methods and achieves substantially more robust performance than the other state-of-the-art methods.

关键词： Big data mutiple scenarios moving object detection

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MOD-SLAM:Visual SLAM with moving object detection in Dynamic Environments

MOD-SLAM:Visual SLAM with Moving Object Detection in Dynamic...

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第40届中国控制会议

作者： Jiarui Hu Hao Fang Qingkai Yang Wenzhong Zha School of Automation Beijing Institute of Technology Information Science Academy China Electronics Technology Group Corporation

In recent years,the significant progress has been made in visual simultaneous localization and mapping（VSLAM）.Many present geometric VSLAM systems rely on static and bright environment assumptions,which is not friendly to the generalization of VSLAM in the real world including a large number of challenging *** cope with these challenges,a real-time and robust visual-inertial SLAM system was proposed,which integrates a neural network for moving object detection（MOD） and greatly reduces the negative influence of dynamic *** has performed an ablation study to validate the effectiveness and necessity of our *** addition,empirical evaluations on typical datasets,as well as in some usual dynamic environments,show that our novel framework can favorably solve the tracking loss,yield pure point cloud and improve the accuracy of VSLAM.

关键词： Visual SLAM moving object detection Dynamic Environments

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