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object tracking-Based “Follow-Me” Unmanned Aerial Vehicle (UAV) System

Computer Systems Science & Engineering 2022年第6期41卷 875-890页

作者： Olubukola D.Adekola Onyedikachi Kenny Udekwu Oluwatobi Tolulope Saliu Damilola Williams Dada Stephen O.Maitanmi Victor Odumuyiwa Olujimi Alao Monday Eze Funmilayo Abibat Kasali Ayokunle Omotunde Department of Software Engineering Babcock UniversityIlisan-RemoOgun StateNigeria Department of Computer Science Babcock UniversityIlisan-RemoOgun StateNigeria Department of Computer Science University of LagosAkokaLagos StateNigeria Department of Computer Science and Mathematics Mountain Top UniversityOgun StateNigeria

The applications of information technology (IT) tools and techniqueshave, over the years, simplified complex problem solving procedures. But thepower of automation is inhibited by the technicality in manning advanced equipment. To this end, tools deliberately combating this inhibition and advancing technological growth are the Unmanned Aerial Vehicles (UAVs). UAVs are rapidlytaking over major industries such as logistics, security, and *** others, this is a very efficient way of carrying out missions unconventional to humans. An application area of this technology is the local film industrywhich is not producing quality movies primarily due to the lack of technicalknow-how in utilizing these systems. This study therefore aim to devise an autonomous object tracking UAV system that would eliminate the complex procedureinvolved in stabilizing an aerial camera (aerial bot) midair and promote the creation of quality aerial video shooting. The study adopted Unified Modeling Language (UML) tools in modeling the system’s functionality. The traditionalServer-Client model architecture was adopted. The OpenCV library employedproved highly efficient in aiding the tracking procedure. The system provided ausable web controller which provides easy interaction between the pilot and thedrone. Conclusively, investments in UAVs would enhance creation of quality graphic contents.

关键词： Artificial intelligence cinematography unmanned aerial vehicles drones object tracking

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object tracking using a convolutional network and a structured output SVM

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Computational Visual Media 2017年第4期3卷 325-335页

作者： Junwei Li Xiaolong Zhou Sixian Chan Shengyong Chen Zhejiang University of Technology Tianjin University of Technology

object tracking has been a challenge in computer vision. In this paper, we present a novel method to model target appearance and combine it with structured output learning for robust online tracking within a tracking-by-detection *** take both convolutional features and handcrafted features into account to robustly encode the target appearance. First, we extract convolutional features of the target by kernels generated from the initial annotated frame. To capture appearance variation during tracking, we propose a new strategy to update the target and background kernel ***, we employ a structured output SVM for refining the target's location to mitigate uncertainty in labeling samples as positive or negative. Compared with existing state-of-the-art trackers, our tracking method not only enhances the robustness of the feature representation, but also uses structured output prediction to avoid relying on heuristic intermediate steps to produce labelled binary samples. Extensive experimental evaluation on the challenging OTB-50 video sequences shows competitive results in terms of both success and precision rate, demonstrating the merits of the proposed tracking method.

关键词： object tracking convolutional network structured learning feature extraction

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object tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

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Intelligent Automation & Soft Computing 2023年第7期37卷 655-675页

作者： Jinping Sun Dan Li Honglin Cheng School of Information Engineering(School of Big Data) Xuzhou University of TechnologyXuzhou221018China

Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion,a tracking algorithm based on multi-time-space perception and instance-specific proposals is proposed to optimize the mathematical model of the correlation filter(CF).Firstly,according to the consistency of the changes between the object frames and the filter frames,the mask matrix is introduced into the objective function of the filter,so as to extract the spatio-temporal information of the object with background ***,the object function of multi-feature fusion is constructed for the object location,which is optimized by the Lagrange method and solved by closed *** the process of filter optimization,the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final track-ing ***,when the tracking results fluctuate,the boundary suppres-sion factor is introduced into the instance-specific proposals to reduce the risk of model drift *** accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks,the object tracking benchmark 2015(OTB2015)and the temple color 128(TC-128).Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is *** distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark,which are better than the results of other competing *** results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.

关键词： Complex scene instance-specific proposals correlation filter multi-time-space perception object tracking

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Learning online structural appearance model for robust object tracking

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Science China(Information Sciences) 2015年第3期58卷 105-118页

作者： YANG Min PEI MingTao WU YuWei JIA YunDe Beijing Laboratory of Intelligent Information Technology School of Computer ScienceBeijing Institute of Technology

The main challenge of robust object tracking comes from the difficulty in designing an adaptive appearance model that is able to accommodate appearance *** tracking algorithms often perform self-updating of the appearance model with examples from recent tracking results to account for appearance ***,slight inaccuracy of tracking results can degrade the appearance *** this paper,we propose a robust tracking method by evaluating an online structural appearance model based on local sparse coding and online metric *** appearance model employs pooling of structural features over the local sparse codes of an object region to obtain a middle-level object *** is then formulated by seeking for the most similar candidate within a Bayesian inference framework where the distance metric for similarity measurement is learned in an online manner to match the varying object *** qualitative and quantitative evaluations on various challenging image sequences demonstrate that the proposed algorithm outperforms the state-of-the-art methods.

关键词： object tracking structural appearance model sparse representation online metric learning

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Real-time object tracking via compressive feature selection

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Frontiers of Computer Science 2016年第4期10卷 689-701页

作者： Kang LI Fazhi HE Xiao CHEN School of Computer Science Wuhan University Wuhan 430072 China

Recently, compressive tracking （CT） has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the structure of the image feature space. A very sparse measurement matrix is used to extract features by multiplying it with the feature vector of the image patch. An adaptive Bayes classifier is trained using both positive samples and negative samples to separate the target from background. On the CT frame- work, however, some features used for classification have weak discriminative abilities, which reduces the accuracy of the strong classifier. In this paper, we present an online compressive feature selection algorithm（CFS） based on the CT framework. It selects the features which have the largest margin when using them to classify positive samples and negative samples. For features that are not selected, we define a random learning rate to update them slowly, It makes those weak classifiers preserve more target information, which relieves the drift when the appearance of the target changes heavily. Therefore, the classifier trained with those discriminative features couples its score in many challenging sequences, which leads to a more robust tracker. Numerous experiments show that our tracker could achieve superior result beyond many state-of-the-art trackers.

关键词： object tracking compressive sensing supervised learning real-time

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Multiple object tracking through Background Learning

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Computer Systems Science & Engineering 2023年第1期44卷 191-204页

作者： Deependra Sharma Zainul Abdin Jaffery Jamia Millia Islamia Department of Electrical EngineeringNew-Delhi110025India

This paper discusses about the new approach of multiple object track-ing relative to background *** concept of multiple object tracking through background learning is based upon the theory of relativity,that involves a frame of reference in spatial domain to localize and/or track any *** of multiple object tracking has seen a lot of research,but researchers have considered the background as ***,in object tracking,the back-ground plays a vital role and leads to definite improvement in the overall process of *** the present work an algorithm is proposed for the multiple object tracking through background *** learning framework is based on graph embedding approach for localizing multiple *** graph utilizes the inher-ent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple *** proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation *** is observed that our proposed algorithm gives better performance.

关键词： object tracking image processing background learning graph embedding algorithm computer vision

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Multiple-object tracking Using Histogram Stamp Extraction in CCTV Environments

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Computers, Materials & Continua 2023年第12期77卷 3619-3635页

作者： Ye-Yeon Kang Geon Park Hyun Yoo Kyungyong Chung Division of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea Contents Convergence Software Research Institute Kyonggi UniversitySuwon16227Korea

object tracking,an important technology in the field of image processing and computer vision,is used to continuously track a specific object or person in an *** technology may be effective in identifying the same person within one image,but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the *** tracking the same object using two or more images,there must be a way to determine that objects existing in different images are the same ***,this paper attempts to determine the same object present in different images using color information among the unique information of the ***,this study proposes a multiple-object-tracking method using histogram stamp extraction in closed-circuit television *** proposed method determines the presence or absence of a target object in an image by comparing the similarity between the image containing the target object and other *** this end,a unique color value of the target object is extracted based on its color distribution in the image using three methods:mean,mode,and interquartile *** Top-N accuracy method is used to analyze the accuracy of each method,and the results show that the mean method had an accuracy of 93.5%(Top-2).Furthermore,the positive prediction value experimental results show that the accuracy of the mean method was 65.7%.As a result of the analysis,it is possible to detect and track the same object present in different images using the unique color of the *** the results,it is possible to track the same object that can minimize manpower without using personal information when detecting objects in different *** the last response speed experiment,it was shown that when the mean was used,the color extraction of the object was possible in real time with 0.016954 *** this,it is possible to detect and track the same object in real time when using the proposed

关键词： Data mining deep learning object detection object tracking real-time object detection multiple object image processing

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Multi object tracking Using Gradient-Based Learning Model in Video-Surveillance

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China Communications 2021年第10期18卷 169-180页

作者： D.Mohanapriya Dr.K.Mahesh Department of Computer Applications Alagappa UniversityKaraikudiTamil Nadu 630003India

On accomplishing an efficacious object tracking,the activity of an object concerned becomes notified in a forthright *** accurate form of object tracking task necessitates a robust object tracking procedures irrespective of hardware *** approaches inferred a vast computational complexity to track an object with high accuracy in a stipulated amount of processing *** the other hand,the tracking gets affected owing to the existence of varied quality diminishing factors such as occlusion,illumination changes,shadows etc.,In order to rectify all these inadequacies in tracking an object,a novel background normalization procedure articulated on the basis of a textural pattern is proposed in this *** preprocessing an acquired image,employment of an Environmental Succession Prediction algorithm for discriminating disparate background environment by background clustering approach have been ***,abstract textural characterizations through utilization of a Probability based Gradient Pattern(PGP)approach for recognizing the similarity between patterns obtained so *** between standardized frame obtained in prior and those processed patterns detects the motion exposed by an object and the object concerned gets identified within a ***,the system is resistant towards illumination *** illumination variation was interpreted in object tracking residing within a dynamic *** approach certainly outperforms other object tracking methodologies like Group Target tracking(GTT),Vi PER-GT,grabcut,snakes in terms of accuracy and average *** PGP-based pattern texture analysis is compared with Gamifying Video object(GVO)approach and hence,it evidently outperforms in terms of precision,recall and F1 measure.

关键词： binary labeling computer vision gradient pattern laplacian operator object tracking

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Robust object tracking via Information Theoretic Measures

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International Journal of Automation and computing 2020年第5期17卷 652-666页

作者： Wei-Ning Wang Qi Li Liang Wang Center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Artificial Intelligence Research Chinese Academy of SciencesQingdao 266300China

object tracking is a very important topic in the field of computer *** sophisticated appearance models have been *** them,the trackers based on holistic appearance information provide a compact notion of the tracked object and thus are robust to appearance variations under a small amount of ***,in practice,the tracked objects are often corrupted by complex noises(e.g.,partial occlusions,illumination variations)so that the original appearance-based trackers become less *** paper presents a correntropy-based robust holistic tracking algorithm to deal with various ***,a half-quadratic algorithm is carefully employed to minimize the correntropy-based objective *** on the proposed information theoretic algorithm,we design a simple and effective template update scheme for object *** results on publicly available videos demonstrate that the proposed tracker outperforms other popular tracking algorithms.

关键词： object tracking information theoretic measures correntropy template update robust to complex noises

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Visual object tracking via Cascaded RPN Fusion and Coordinate Attention

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Computer Modeling in Engineering & Sciences 2022年第9期132卷 909-927页

作者： Jianming Zhang Kai Wang Yaoqi He Lidan Kuang School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China

Recently,Siamese-based trackers have achieved excellent performance in object ***,the high speed and deformation of objects in the movement process make tracking ***,we have incorporated cascaded region-proposal-network(RPN)fusion and coordinate attention into Siamese *** proposed network framework consists of three parts:a feature-extraction sub-network,coordinate attention block,and cascaded RPN *** exploit the coordinate attention block,which can embed location information into channel attention,to establish long-term spatial location dependence while maintaining channel ***,the features of different layers are enhanced by the coordinate attention *** then send these features separately into the cascaded RPN for classification and *** to the two classification and regression results,the final position of the target is *** verify the effectiveness of the proposed method,we conducted comprehensive experiments on the OTB100,VOT2016,UAV123,and GOT-10k *** with other state-of-the-art trackers,the proposed tracker achieved good performance and can run at real-time speed.

关键词： object tracking deep learning coordinate attention cascaded RPN

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