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7th International Conference on Computational Collective Intelligence (ICCCI)

作者： Silar, Zdenek Dobrovolny, Martin Univ Pardubice Fac Elect Engn & Informat Pardubice 53002 Czech Republic

ISBN: (纸本)9783319240695;9783319240688

In this article is presented algorithm for obstacle detection and objects tracking in a railway crossing area. The object tracking is based on template matching and sum of absolute differences. The object tracking was implemented for better reliability of presented system. For optical flow estimation is used a modified Lucas-Kanade method. The results of proposed algorithm were verified in a real traffic scenarios consisted of two railway crossings in Czech Republic during 2013-14 under different environmental conditions.

关键词： Level crossing monitoring objects detection Optical flow velocity vectors Template matching objects tracking K-means clustering

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objects detection and Recognition in Videos for Sequence Learning 18

Objects Detection and Recognition in Videos for Sequence Lea...

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18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Wang, Yingxu Cai, Tony Zatarain, Omar Univ Calgary Int Inst Cognit Informat & Cognit Comp ICIC Dept Elect & Comp Engn Schulich Sch Engn 2500 Univ Dr NW Calgary AB T2N 1N4 Canada Univ Calgary Int Inst Cognit Informat & Cognit Comp ICIC Dept Elect & Comp Engn Hotchkiss Brain Inst 2500 Univ Dr NW Calgary AB T2N 1N4 Canada

ISBN: (纸本)9781728114194

A key challenge to sequence learning for video comprehension is objects detection and localization in dynamic and real-time environment. This paper presents two methodological approaches to autonomous and generic object detection and localization in video sequences. Algorithms for both facial and non-facial object localization, as well as their integration, are developed. A set of experiments and case studies for practical video image processing is demonstrated for sequence learning. This work paves a way to sequence learning towards enhanced computer and robot vision technologies in applications of self-driving cars and real-time facial recognition.

关键词： Sequence learning visual sequence learning autonomous learning objects detection object localization object extraction video sequence processing methods algorithms experiments

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objects detection of UAV for Anti-UAV Based on YOLOv4 2

Objects Detection of UAV for Anti-UAV Based on YOLOv4

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2nd IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Shi, Qingbang Li, Jun Guilin Univ Elect Technol Guilin Sch Elect Engn & Automat Guilin 541000 Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guangxi Key Lab Optoelect Informat Proc Guilin 541000 Peoples R China

ISBN: (纸本)9781728199481

Currently, drone detection is a research hotspot in the security field due to its popularity. This paper proposed a recognition method of the low-altitude drone detection based on the YOLOv4 (You Only Look Once version 4) model and also introduced YOLOv4 algorithm in detection low-altitude UAV object for the first time. Sample set of drone flight attitude images constructed by shooting, downloading from the Internet and expanding the existing data is used to solve the lack of standard data set. The experimental results show that although YOLOv4, YOLOv3 and SSD algorithm all belong to one-stage algorithm, the average accuracy and real-time detection speed of YOLOv4 are better than that of the YOLOv3 and SSD.

关键词： Anti-CAV objects detection YOLOv4 CNN (convolutional neural network)

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objects detection Method by Learning Lifted Wavelet Filters 6

Objects Detection Method by Learning Lifted Wavelet Filters

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6th Chinese Conference on Pattern Recognition (CCPR)

作者： Abulikemu, Aireti Yushan, Aliya Turki, Turghunjan Abdukirim Osman, Abdurusul Xinjiang Normal Univ Sch Math Sci Urumqi Peoples R China

ISBN: (纸本)9783662456460;9783662456453

A fast objects detection method is proposed, which is based on the variance-maximization learning of lifting dyadic wavelet filters. First, we derive a difference equation from two kinds of lifting high-pass components of a target image. The difference equation is an approximation of an inverse problem of an elliptic equation, which includes free parameters of the lifting filter. Since this discrete inverse problem is ill-conditioned, the free parameters are learned by using the least square method and a regularization method. objects detection is done by applying the learned lifting filter to a query image.

关键词： Lifting wavelet filter Difference equation Inverse problem Regularization Learning objects detection

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objects detection Based on Multi-BING Feature Model 2

Objects Detection Based on Multi-BING Feature Model

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2nd International Conference on Information Technology and Intelligent Transportation Systems (ITITS)

作者： Su Yue Chen Mianshu Sang Aijun Li Xiaoni Li Mengying Jilin Univ Sch Commun Engn Changchun 130000 Jilin Peoples R China

ISBN: (纸本)9781614997856;9781614997849

objects detection is not only an important research direction in computer vision, but also the basis of object tracking and behavior detection. In this paper, we analyze the characteristics and principles of Binarized normed gradients (BING) algorithm, and propose an objects detection algorithm based on multi-BING feature model. At first, the proposed algorithm uses K-means clustering algorithm to cluster the training data, and then trains each category of images to establish the corresponding BING feature model. At the detection stage, multiple BING feature models are respectively used for testing. We collected all the detected results from all models as the final detection results. Experimental results demonstrate that the proposed algorithm effectively improves the object detection rate (DR) under various overlaps at the expense of a small amount of time by generating a small set of high-quality proposals.

关键词： Computer vision objects detection BING K-means clustering

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Vulnerable objects detection for autonomous driving: A review

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INTEGRATION-THE VLSI JOURNAL 2021年 78卷 36-48页

作者： Khatab, Esraa Onsy, Ahmed Varley, Martin Abouelfarag, Ahmed Univ Cent Lancashire Preston PR1 2HE Lancs England Arab Acad Sci Technol & Maritime Transport Alexandria 21923 Egypt

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.

关键词： Autonomous driving objects detection Sensor fusion Deep learning

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Box-based Codebook Model for Real-time objects detection

Box-based Codebook Model for Real-time Objects Detection

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7th World Congress on Intelligent Control and Automation

作者： Tu, Qiu Xu, Yiping Zhou, Manli Huazhong Univ Sci & Technol Elect & Informat Engn Dept Wuhan 430074 Hubei Province Peoples R China

ISBN: (纸本)9781424421138

Background modeling is the key technique of video surveillance. An improved box-based codebook modeling method is presented. The background model is constructed by encoding each pixel into a codebook consists of codewords based on a box model. It can handle cluttered background and allow moving objects in the training video sequences. The box-based codebook model representation is efficient in memory and speed compared with other background modeling techniques. The experiment results show the speed of building the codebook model and detecting objects over other methods.

关键词： box-based codebook model background subtraction objects detection codebook algorithm

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Box-based Codebook Model for Real-time objects detection

Box-based Codebook Model for Real-time Objects Detection

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.19

作者： Qiu Tu Yiping Xu Manli Zhou Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan Hubei China

关键词： Box-based codebook model Background subtraction objects detection Codebook algorithm

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Research on Abandoned and Removed objects detection Based on Embedded System

Research on Abandoned and Removed Objects Detection Based on...

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IEEE International Conference on Intelligent Transportation Systems

作者： Bin Hu Yiyue Li Zizhang Chen Gang Xiong Fenghua Zhu Cloud Comput. Center Dongguan China

ISBN: (纸本)9781479960798

This paper presents a real-time detection algorithm which detects abandoned objects in embedded Intelligent Video Surveillance (IVS) System. This algorithm uses two different Gaussian mixture models (GMM) with different learning rates to extract the foreground in order to detect the abandoned objects and output alarms. Experimental results show the algorithm is robust and well-performed in different circumstances.

关键词： Gaussian processes embedded systems mixture models object detection video surveillance GMM Gaussian mixture models IVS system abandoned object embedded intelligent video surveillance system embedded system learning rate objects detection real-time detection algorithm Conferences Intelligent transportation systems

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Comparative Performance of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for Layout Analysis of Historical Documents Images

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APPLIED SCIENCES-BASEL 2025年第6期15卷

作者： dos Santos Junior, Eder Silva Paixao, Thuanne Alvarez, Ana Beatriz Univ Acre UFAC PAVIC Lab BR-69915900 Rio Branco Brazil

The digitalization of historical documents is of interest for many reasons, including historical preservation, accessibility, and searchability. One of the main challenges with the digitization of old newspapers involves complex layout analysis, where the content types of the document must be determined. In this context, this paper presents an evaluation of the most recent YOLO methods for the analysis of historical document layouts. Initially, a new dataset called BHN was created and made available, standing out as the first dataset of historical Brazilian newspapers for layout detection. The experiments were held using the YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures. For training, validation, and testing of the models, the following historical newspaper datasets were combined: BHN, GBN, and Printed BlaLet GT. Recall, precision, and mean average precision (mAP) were used to evaluate the performance of the models. The results indicate that the best performer was YOLOv8, with a Recalltest of 81% and an mAPtest of 89%. This paper provides insights on the advantages of these models in historical document layout detection and also promotes improvement of document image conversion into editable and accessible formats.

关键词： historical documents layout analysis deep learning objects detection YOLO

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