A Real-Time Implementation of Moving Object Action Recognition System Based on Motion Analysis

Sehairi, Kamal; Benbouchama, Cherrad; Chouireb, Fatima

doi:10.11591/ijeei.v5i1.261

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…A smart video surveillance system has three major stages: detection, classification, and activity recognition [14]. Happening of abnormal events in public places is budding at an extraordinary rate, from closed-circuit security systems to contemporary systems that can monitor individuals at airports, subways, concerts, sporting events etc., to network of cameras covering vital locations within a city.…”

Section: Fig 3 a Frame Work For General Abnormal Events Detection Mmentioning

confidence: 99%

Frameworks for Animal and Human Detection using Camera Images to Detect Anomaly

P*¹,

Krishnakumar²

2019

IJITEE

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Human and animal interacting events in video image frames will leads to anomalies and it cannot be predicted. These anomalous events was happened due to the interactions of human, animals and birds with each other. Some of the human and animal anamolous interactions will leads to anomalous actions in camera surveillance sites and are to be considered as a serious issue. All anomalies will leads the system or the authorities who dedicated for monitoring to the suspicious events. To detect the anomalies, the animals and human objects must be identified in each image frames first. After object identification events detection phase has to be done. Before going to the event detection phase, noise elimination, shadow detection, object classification can be carried out according to the needs. This paper reveals general detection methodology for animal detection and human crowd. The proposed work mainly concentrated on the detection of animals in human territory based on its palm print images and video images. Human crowd can be treated as smoke- screen for all types of anomalies and so this paper mainly concentrated to detect the crowd well in advance and thereby preventing the crowd before it is happening

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Section: Fig 3 a Frame Work For General Abnormal Events Detection Mmentioning

confidence: 99%

Frameworks for Animal and Human Detection using Camera Images to Detect Anomaly

P*¹,

Krishnakumar²

2019

IJITEE

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“…Compression of the captured video has received significant interest in literature. The availability of high-resolution CMOS image sensors at low cost makes the WVSNs more trending [1], especially for real-time surveillance and environment monitoring [2,3] and medical applications [4], etc. The new applications of WVSN are emerging very rapidly and demanding efficient pre-processing and transmission [5].…”

Section: Introductionmentioning

confidence: 99%

Fast Side Information Generation for High-Resolution Videos in Distributed Video Coding Applications

Khursheed¹,

Badruddin²,

Jeoti³

et al. 2020

IJACSA

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Distributed video coding (DVC) is an attractive and promising scheme that suits the constrained video applications, such as wireless sensor networks or wireless surveillance systems. In DVC, estimation of fast and consistent side information (Տ į) is a critical issue for instant and real-time decoding. This issue becomes even more serious for highresolution videos. Therefore, to minimise the side information estimation computational complexity, in this work, a computationally low complex DVC codec is proposed, which uses a simple phase interpolation (Phase-I) algorithm. It performs faster for all resolutions videos, and significant results are achieved for high-resolution videos with a large group of pictures (GOP). For the proposed technique, the computation time rapidly decreases with an increase in resolution. It performs 221% to 280% faster from conventional frame interpolation method for high-resolution videos and large GOP at the cost of little degradation in the visual quality of estimated side information.

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“…In [15], a real-time moving object action recognition system was proposed based on motion analysis. The system was implemented on a PixelStreams-based FPGA.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Abnormal Behaviour Detection Using Trajectory Analysis

Shuaau

Thang

Lai

2019

IJECE

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<span lang="EN-GB">Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different anomalies such as entering staff area, running, loitering and squatting down. The developed anomaly detection algorithms were tested on videos captured at Asia Pacific University’s reception area. These algorithms were able to achieve a promising detection accuracy of 89% and a false positive rate of 4.52%.</span>

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A Real-Time Implementation of Moving Object Action Recognition System Based on Motion Analysis

Cited by 3 publications

References 17 publications

Frameworks for Animal and Human Detection using Camera Images to Detect Anomaly

Frameworks for Animal and Human Detection using Camera Images to Detect Anomaly

Fast Side Information Generation for High-Resolution Videos in Distributed Video Coding Applications

Autonomous Abnormal Behaviour Detection Using Trajectory Analysis

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