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An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3

Intelligent Automation & Soft Computing 2023年第2期35卷 1295-1310页

作者： Mubashir Javaid Muazzam Maqsood Farhan Aadil jibran safdar Yongsung Kim Department of Computer Science UETTaxilaPakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Technology Education Chungnam National UniversityDaejeon34134Korea

Currently,worldwide industries and communities are concerned with building,expanding,and exploring the assets and resources found in the oceans and *** precisely,to analyze a stock,archaeology,and surveillance,sev-eral cameras are installed underseas to collect ***,on the other hand,these large size videos require a lot of time and memory for their processing to extract relevant ***,to automate this manual procedure of video assessment,an accurate and efﬁcient automated system is a greater *** this perspective,we intend to present a complete framework solution for the task of video summarization and object detection in underwater *** employed a perceived motion energy(PME)method toﬁrst extract the keyframes followed by an object detection model approach namely YoloV3 to perform object detection in underwater *** issues of blurriness and low contrast in underwater images are also taken into account in the presented approach by applying the image enhancement ***,the suggested framework of underwater video summarization and object detection has been evaluated on a publicly available brackish *** is observed that the proposed framework shows good performance and hence ultimately assists several marine researchers or scientists related to theﬁeld of underwater archaeology,stock assessment,and surveillance.

关键词： Computer vision deep learning digital image processing underwater video analysis video summarization object detection YOLOV3

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A Triplet-Branch Convolutional Neural Network for Part-Based Gait Recognition

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Computer Systems Science & Engineering 2023年第11期47卷 2027-2047页

作者： Sang-Soo Yeo Seungmin Rho Hyungjoon Kim jibran safdar Umar Zia Mehr Yahya Durrani Department of Computer Engineering Mokwon UniversityDaejeonKorea Department of Industrial Security Chung-Ang UniversitySeoul06974Korea Department of Computer Engineering Semyung UniversityJechun-siKorea Department of Computer Science UET TaxilaTaxilaPakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan

Intelligent vision-based surveillance systems are designed to deal with the gigantic volume of videos captured in a particular environment to perform the interpretation of scenes in form of detection,tracking,monitoring,behavioral analysis,and *** addition to that,another evolving way of surveillance systems in a particular environment is human gait-based *** the existing research,several methodological frameworks are designed to use deep learning and traditional methods,nevertheless,the accuracies of these methods drop substantially when they are subjected to covariate *** covariate variables disrupt the gait features and hence the recognition of subjects becomes *** handle these issues,a region-based triplet-branch Convolutional Neural Network(CNN)is proposed in this research that is focused on different parts of the human Gait Energy Image(GEI)including the head,legs,and body separately to classify the subjects,and later on,the final identification of subjects is decided by probability-based majority voting ***,to enhance the feature extraction and draw the discriminative features,we have added soft attention layers on each branch to generate the soft attention *** proposed model is validated on the CASIA-B database and findings indicate that part-based learning through triplet-branch CNN shows good performance of 72.98%under covariate conditions as well as also outperforms single-branch CNN models.

关键词： Vision-based surveillance systems deep learning triplet-branch CNN gait recognition covariate conditions

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