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Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

Geo-Spatial Information Science 2019年第1期22卷 23-34页

作者： Ali Almagbile Department of Geography Yarmouk UniversityIrbidJordan

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.***.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.

关键词： Unmanned Aerial Vehicle(UAV) crowd density corner detection Feature from Accelerated Segment Test(FAST)algorithm clustering analysis

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Image corner detection using topology learning

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The Journal of China Universities of Posts and Telecommunications 2010年第6期17卷 101-105页

作者： SUN Wei,YANG Xuan College of Computer Science and Software Engineering,Shenzhen University,Shenzhen 518060,China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China

Image corner detection plays an important role in image analysis and recognition. This paper presents a novel corner detector based on the growing neural gas （GNG） network and this proposed detector is called GNG-C. With the GNG network,image topology information can be learned and used to implement corner detection. The GNG-C approach can be described as consisting of the following steps. First,a canny edge detector is used to acquire the contour information of the input image. This edge information is used to train a modified GNG network. A special stopping criterion is defined to terminate network learning. Second,vectors formed between network nodes and their neighbors are used to measure curvatures. Third,dynamic regions of support （ROS） are determined based on these curvatures. These ROS are used to suppress curvature noise. The curvature values of the nodes are then analyzed to estimate the candidate corners. Finally,the candidates are distilled by a non-maxima suppression process to obtain the final set of corners. Experiments on both artificial and real images show that the proposed corner detection method is feasible and effective.

关键词： corner detection GNG topology learning self-organizing map

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Subpixel accuracy for extracting groove center based on corner detection

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China Welding 2006年第3期15卷 59-63页

作者：刘苏宜王国荣石永华 College of Mechanical Engineering South China University of Technology

Subpixel accuracy for V-groove center in robot welding is researched and a software measure to increase the accuracy of seam tracking by laser is presented. LOG （ Laplacian of Gaussian ） operator is adopted to detect image edge. Vgroove center is extracted by corner detection of extremum curvature. Subpixel position is obtained by Lagarange polynomial interpolation algorithm. Experiment results show that the method is brief and applied, and is sufficient for the real time of robot welding by laser sensors.

关键词： edge detection LOG operator corner detection subpixel polynomial interpolation

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Pulp Fiber corner detection Arithmetic Based on Local Curvature Function

Pulp Fiber Corner Detection Arithmetic Based on Local Curvat...

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第二十七届中国控制会议

作者： Gao Hui 1,Qiu Shubo 1,2 1.School of Electronic Information and Control Engineering,Shandong Institute of Light Industry,Jinan 250353,P.R.China 2.School of Electronic Information and Control Engineering,Shandong Institute of Light Industry,Jinan 250353,P.R.China

The key problem of fiber shape detection is the orientation of its corner;corner is the pivotal point which determines the shape feature of fiber curve.In order to describe the bent character of pulp fiber image,pulp fiber corner detection arithmetic based on local curvature function was proposed.After extracting edge by Canny operator,the local curvature function of contour was calculated,corners are the peaks of the function and detected by threshold.Experimental results show that the arithmetic is precisely,fast and effectively during the corner detection of pulp fiber.The arithmetic is very stable and robust.

关键词： corner detection Pulp Fiber Local Curvature Function Feature Extraction Image Processing

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A Parallel Algorithm for corner detection on Object Contour

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Journal of Systems Engineering and Electronics 1992年第1期3卷 56-63页

作者： Lan YongchuanBeijing Institute of Remote Sensing Equipment,Beijing P.O.Box 3925 Beijing Institute of Remote Sensing Equipment Beijing P.O. Box 3925

A new parallel algorithm for corner detection on object contour is presented in the paper. In this algorithm whenever a point (pixel) is scanned, the k direction codes between the two sides of the point, which is on the edge of an object, are obtained by k-step forward and backward boundary tracking. A comer is determined by the sum of the difference between the two weighted code chains. Note that the whole chain code sequence or boundary of an object is not necessary to be extracted at all in this algorithm, and the corners are obtained immediately once the image is scanned, furthermore, what humans perceive as corners can be detected and localized by this algorithm.

关键词： corner detection Boundary tracking Degree of sharpness

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Method for segmentation of overlapping fish images in aquaculture

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International Journal of Agricultural and Biological Engineering 2019年第6期12卷 135-142页

作者： Chao Zhou Kai Lin Daming Xu Jintao Liu Song Zhang Chuanheng Sun Xinting Yang Beijing Research Center for Information Technology in Agriculture Beijing 100097China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China National Engineering Laboratory for Agri-product Quality Traceability Beijing 100097China Beijing Fisheries Research Institute Beijing 100068China Department of Computer Science University of AlmeriaAlmeria04120Spain

Individual fish segmentation is a prerequisite for feature extraction and object identification in any machine vision system.In this paper,a method for segmentation of overlapping fish images in aquaculture was proposed.First,the shape factor was used to determine whether an overlap exists in the picture.Then,the corner points were extracted using the curvature scale space algorithm,and the skeleton obtained by the improved Zhang-Suen thinning algorithm.Finally,intersecting points were obtained,and the overlapped region was segmented.The results show that the average error rate and average segmentation efficiency of this method was 10%and 90%,respectively.Compared with the traditional watershed method,the separation point is accurate,and the segmentation accuracy is high.Thus,the proposed method achieves better performance in segmentation accuracy and effectiveness.This method can be applied to multi-target segmentation and fish behavior analysis systems,and it can effectively improve recognition precision.

关键词： aquaculture image processing overlapping segmentation corner detection improved Zhang-Suen algorithm

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An Asteroid Landing Location Algorithm Based on Image Feature

An Asteroid Landing Location Algorithm Based on Image Featur...

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第25届中国控制与决策会议

作者： Li Jiangeng Zhang Rong Wei Ruoyan Zhang Yu College of Electronic Information and Control Engineering Beijing University of Technology

ISBN: (纸本)9781467355339

Optical navigation technology is widely used in precision guidance for autonomous landing and hazard avoidance. According to the requirements of the computer vision landing location system on a planetary lander, this paper proposed an algorithm to automatically obtain the precise landing position, which is based on optical image features. The main contents of this paper are as follows: Firstly, an integrated corner detection algorithm is firstly applied to detect the interest corners on asteroid images, which performs quite well compared with other feature extraction methods in many aspects;Secondly, the paper introduces a mutual matching method based on moment invariant and regional feature mutual information;Finally, the landing position can be established by calculating the vectors between the goal landing point and the feature points obtained on the base image and correctly matching the feature point pairs on the base image with images captured by CCD camera in real time when landing on the asteroid. The simulated experiments have been conducted using Eros Asteroid images. The results show that the algorithm can achieve high matching accuracy and is qualified for engineering application.

关键词： corner detection feature matching regional mutual information and precision landing

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A Multi-Object Grasping detection Based on the Improvement of YOLOv3 Algorithm

A Multi-Object Grasping Detection Based on the Improvement o...

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第32届中国控制与决策会议

作者： Kun Du Jilai Song Xiaofeng Wang Xiang Li Jie Lin Faculty of Robot Science and Engineering Northeastern University Shenyang SIASUN Robot & Automation Co. LTD. State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

YOLOv3 has achieved good results in the field of object detection.In order to achieve multi-object grasping detection,the network structure has been improved.The improved YOLOv3 algorithm is applied to the object position and pose detection in robotic grasping,and a deep learning model is proposed to predict the robot’s grasping position,which can detect the occurrence of multiple objects in real time and grasp them in order according to the semantic information.For the specific application scenario,the corresponding dataset is made,and a corner detection method based on YOLOv3 is proposed to grasping position and pose detection.Compared with the traditional corner detection method,this method has semantic information in its detected corner.In the scene,we first classify and locate the object,then detect the corner of the object,and filter the corner of the false detection through the positioning of the object,and design the corresponding algorithm to complete the corner of the missed detection,so that the accuracy of the corner detection is greatly improved,reaching 99% in the self-made dataset.Finally,the position information of the corner is used to calculate the centroid position of the object,that is,the grasping point of the object.The point cloud information is obtained by depth camera,and the grasping pose of the object is calculated.This method can greatly improve the accuracy of grasping detection in specific scenes.

关键词： YOLOv3 Robotic grasping Deep learning corner detection Grasping position and pose detection

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An Extracting Method of corner Points from Laser Sensor Readings

An Extracting Method of Corner Points from Laser Sensor Read...

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第37届中国控制会议

作者： Dingyao Ji Jin Cheng Yuan Xu School of Electrical Engineering University of Jinan

Simultaneous Localization and Mapping（SLAM） is a hot and difficult point in recent years. There are a large number of corner structures that are generated by two walls in the environment. corner points can easily solve Simultaneous Localization and Mapping tasks of mobile robots. This method is divided into four parts: extraction points, line segment extraction, corner point extraction and corner point feature match. First, the map environment is scanned by the laser sensor to store the information of the environment. Second, the IEFT and the least square method are used to extract the straight line from the existing points.After that, we find corner points by finding the intersection point. At last, corner points are matched by angle and distance. Result proves that the method in the article can accurately extract the corner features. This method is robust and suitable for various environments.

关键词： Laser sensor extracting line segments corner detection matching corner

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Autonomous following indoor omnidirectional mobile robot

Autonomous following indoor omnidirectional mobile robot

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第30届中国控制与决策会议

作者： Kaibo Zhang Lei Zhang Academy of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

This paper designed a follow-up indoor omni-directional mobile robot which can achieve any target indoor tracking.Compared with the existing scheme,this robot does not require target to wear specific markers in the autonomous tracking process,and also does not need any auxiliary markers.Only the ordinary camera on the robot body is needed,and the target is tracking by using the Shi-Tomasi algorithm and the Lucas-Kanade algorithm,the cost is relatively low,a wide range of applications.Users can choose their own tracking target of robot,which can be human or object,regardless of the state of tracking the target’s movement,the robot can be moved to position away from the target setting.Finally,this paper verifies the feasibility of the system through experiments.

关键词： Visual tracking Omni-directional mobile robot corner detection

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