Moving Target Detection Algorithm Based on Susan Edge Detection and Frame Difference

Fan, Xinfeng; Cheng, Yijie; Fu, Qiang

doi:10.1109/icisce.2015.78

Cited by 19 publications

(4 citation statements)

References 3 publications

(4 reference statements)

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“…The purpose of this step is to segment the input image and obtain preliminary road regions. The SUSAN (Smallest Univalue Segment Assimilating Nucleus) [35] algorithm is adopted to segment images. SUSAN algorithm is the representative of template matching, which was proposed by Smith and Brady.…”

Section: Methodsmentioning

confidence: 99%

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Sun

Zhang

2019

Wireless Communications and Mobile Computing

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Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Sun

Zhang

2019

Wireless Communications and Mobile Computing

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“…In contrast to the moving object detection algorithms used for fixed cameras, such as optical flow [9], inter-frame difference [10], and background modeling [11], the image background often appears to undergo rotation, translation, and scaling when employing moving cameras. Therefore, it is difficult to model the background or detect moving targets based on the optical flow.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection Based on Background Compensation and Deep Learning

Zhu

Wang

et al. 2020

Symmetry

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Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.

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“…The process of searching neighbor edge points is ameliorated, depending on these global optimized points. Fan et al extended the SUSAN operator to detect edges in a moving target detection process [108]. The frame difference for moving target detection was gathered after detecting edges.…”

mentioning

confidence: 99%

Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

et al. 2021

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Face detection, which is an effortless task for humans, is complex to perform on machines. The recent veer proliferation of computational resources is paving the way for frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is little attention paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. First, we explore a wide variety of the available face detection algorithms in five steps, including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all-inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of the neural network. We present detailed comparisons among the algorithms in all-inclusive and under sub-branches. We provide the strengths and limitations of these algorithms and a novel literature survey that includes their use besides face detection.

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Moving Target Detection Algorithm Based on Susan Edge Detection and Frame Difference

Cited by 19 publications

References 3 publications

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Moving Object Detection Based on Background Compensation and Deep Learning

Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

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