Intelligent video surveillance system

Banu, Virgil Claudiu; Costea, Ilona Mădălina; Nemtanu, Florin Codrut; Badescu, I.

doi:10.1109/siitme.2017.8259891

Cited by 22 publications

(6 citation statements)

References 2 publications

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Section: Smart Cardmentioning

confidence: 99%

“…• Vehicles monitoring -Number Plate Recognition • Human monitoring -Face Recognition [22], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66] Smart Energy • Energy Sustainability • Energy Management System • Smart Streets Lights [8], [3], [13], [68], [69], [71], [72], [73], [74], [75], [76], [67], [77], [78], [79], [80],…”

Section: Smart Cardmentioning

confidence: 99%

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Student Perception Study on Smart Campus: A Case Study on Higher Education Institution

Mustafa

Isa

Rauf

et al. 2021

MJCS

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With the current development of science and technology, big data technology, the Internet of Things (IoT), cloud computing, and mobile Internet in Malaysia, it is time for smart campuses to be established and Higher Education Institution (HEI) should not have left behind. The smart campus is the upgraded version of a digital university, providing a better academic environment to the lecturers and students. A smart campus enhances education, research, design, and delivering the appropriate higher learning modules to pursue the latest developments in ICT technology that can drive future educational growth and innovation. In short, the smart university is to construct a safe and secure, stable, environmentally-friendly, and energy-saving campus. However, so far, there is no standard smart campus concept to follows. This research paper evaluates the student's perceptions on the smart campus from the various application. A total of 912 students from local HEI have participated as valid observations for data analysis. The results show the students have selected 11 applications as high impact based on the Relative Importance Index for smart campus implementation. Future work could focus on the feedback from the staff and lecturers' points of view to propose a solid conceptual smart campus framework.

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Section: Smart Cardmentioning

confidence: 99%

Section: Smart Cardmentioning

confidence: 99%

Student Perception Study on Smart Campus: A Case Study on Higher Education Institution

Mustafa

Isa

Rauf

et al. 2021

MJCS

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“…Tracking mobile object is a challenging and one of the important topics in the computer vision. Various applications were lead in this task, such as traffic monitoring [1], video surveillance [2], auto driving, braking assistance and human behavioral analysis [3]. Despite the existing research in the past decades, building a robust and effective tracking system continues to be a wide area of research in this digital area.…”

Section: Introductionmentioning

confidence: 99%

Self scale estimation of the tracking window merged with adaptive particle filter tracker

Azdoud

Aouatif²,

Bouchra³

et al. 2023

IJECE

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Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.

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“…There are many different types of Closed Circuit Television (CCTV) systems available, analogue and digital, wired and wireless and their modes of operation vary; however, the basic components are more or less the same: a camera, a lens, a monitor, and (for wired systems) cables that carry the signal from one place to another. Many systems also use video recorders to record the video footage [1].…”

Section: Introductionmentioning

confidence: 99%

System design of video surveillance

Abdulhamid

Murungi

2019

SRIT

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This paper describes the steps involved in designing of a video surveillance system. It discusses the theory of video surveillance types, components involved, selection of the best equipment, and also a detailed virtual design. An introduction of the concept of video surveillance systems is followed by the detailed discussion of design considerations and the design verification. The system is designed to monitor a bank floor where the monitor displays the desired output from a simulated implementation of the system.

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Intelligent video surveillance system

Cited by 22 publications

References 2 publications

Student Perception Study on Smart Campus: A Case Study on Higher Education Institution

Student Perception Study on Smart Campus: A Case Study on Higher Education Institution

Self scale estimation of the tracking window merged with adaptive particle filter tracker

System design of video surveillance

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