Research on motion detection of Video Surveillance System

Wang, Zhengjie; Zhao, Yuan; Zhang, Jifen; Guo, Yinjing

doi:10.1109/cisp.2010.5647987

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…In the background subtraction technique of a motion detection, the video sequence is composed of a series of video images which contains the features of geometry information of the target, extract relevant information to analyze the motion of targets then get detection results. The compression ratio was greatly improved [13].…”

Section: Background Phase Subtraction Methodsmentioning

confidence: 99%

“…With the development of technology, the functions of the intelligent inspection robot are gradually improved, making it simulate manual operation in the application process and replace the traditional manual inspection [8][9][10][11]. The intelligent inspection robot itself carries related equipment such as infrared thermal imaging cameras, which can identify and control the environmental factors of the automatic and manual inspection, fire warning, and fire protection during the inspection process [12][13][14][15][16][17][18][19][20]. The intelligent inspection robot is in the stage of rapid development, and its use scope is constantly increasing, but there are a series of problems, such as poor positioning accuracy, insufficient anti-interference ability, and difficulty in real-time tracking.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Robot’s Intelligent Inspection, Its Target Detection Method

Lu¹,

Badarch²

2022

AJCST

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Intelligent inspection robot has the characteristics of programmable, and can be applied to various inspection environments. In general, studies show the present trend is to replace humans with an inspection robot thereby reducing the risks and improving the inspection efficiency. The intelligent inspection robot is based on intelligent technology and has programmability. In this paper, based on the research of intelligent inspection robot technology, we analyze inspection techniques, their algorithms, functions, characteristics and other important parameters. The research mainly focuses on two things: the target detection, methods and improved Adaboost algorithm to improve the accuracy of target detection; the Camshift algorithm which is improved to complete tracking design, timely data acquisition, timely problem discovery and timely solution. The target detection and target tracking are studied and their algorithms are analyzed. We present that a tracking algorithm based on improved Camshift deals with the problems which exist in traditional Camshift algorithm. In addition, we present Meanshift algorithm improves the Camshift algorithm for the whole-process tracking, automation and intelligence level, and efficient tracking. Next, combined with relevant technologies and techniques, the algorithm is improved to complete the target detection design and tracking design, and to solve the problems of inaccurate target detection and untimely detection.

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Section: Background Phase Subtraction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on the Robot’s Intelligent Inspection, Its Target Detection Method

Lu¹,

Badarch²

2022

AJCST

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“…Pentingnya dan popularitas analisis gerak telah menghasilkan beberapa penelitian sebelumnya seperti yang telah ditunjukan oleh wang dan Zao [5] menggunakan background subtraction untuk Analisis gambar, misalnya, melanjutkan analisis gerak area sensitif yang ditentukan dalam rentang gambar dan menyediakan fungsi video, pelacakan target, dan hubungan alarm tertentu. Dalam urutan video ini terdiri dari rangkaian gambar video yang berisi fitur informasi geometri target, mengekstrak informasi yang relevan untuk menganalisis gerakan target kemudian mendapatkan hasil deteksi.…”

Section: Related Workunclassified

“…Gambar 1 : Flowchart deteksi gerakan [5] Pada penelitian lain Rikebe dan Patil [6] menyajikan deteksi gerakan dengan mengembangkan algoritma baru berdasarkan algoritma pengurangan latar belakang. Dalam model latar belakang pertama yang dapat diandalkan berdasarkan statistik digunakan.…”

Section: Related Workunclassified

Moving Object Detection on CCTV Surveillance Using the Frame Difference Method

Saluky¹

2019

ITEJ

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In today's computer vision research, many build systems for observing humans and understanding their appearance, activities, and behaviour that provide sophisticated interfaces for interacting with humans, and create plausible human models for various purposes. This paper presents a simple algorithm for detecting moving objects from a static background based on frame differences. First, the first frame is captured via a static camera such as Closed Circuit Television (CCTV) after which a sequence of frames is taken periodically. Second, the absolute difference is calculated between successive frames and the difference in images is stored in the system. Third, the difference image is converted into a grey image and then translated into a binary image. Finally, morphological filtering is carried out to remove noise. In the last process, moving objects can be detected in conditions that do not change much apart from moving objects.

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