Suspicious Loitering Detection from Annotated CCTV Feed Using CEP Based Approach

Shahad, Rabiah Adawiyah; Ibrahim, Mohd Faisal; Xian, Ezra Lim Kai; Hussain, Aini; Saad, Mohamad Hanif Md

doi:10.17576/jkukm-2018-30(1)-11

Cited by 3 publications

(2 citation statements)

References 18 publications

(19 reference statements)

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“…As outlined by Shahad et al, it is crucial for intelligent surveillance systems to understand and predict events such as burglary and robbery in a monitored area. Hence, a system based on the Complex Event Processing (CEP) approach was developed to follow previous event patterns and detect anomalous activities [28]. A similar system was designed by Shao et al using big data for security event detection.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Camera Surveillance System with Effective Notification Features

Khodadin¹,

Pudaruth²

2020

IJCDS

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The focal point of this work is to develop an intelligent camera surveillance system which englobes the key functionalities of existing surveillance systems. Other than regular functionalities such as motion detection, object detection, face recognition and counting people, it also integrates a novel and advanced object displacement detection feature to provide more security by determining if an object has been displaced by an intruder. When people are detected, a counting module displays the number of persons present in the surveillance area. A face recognition module distinguishes between authorised and unauthorised users. This biometric functionality reduces false alarms which makes the system more robust. An object detection module detects certain valuable objects such as handbags, laptops and smartphones. Also, images and short video recordings are stored on the cloud. Furthermore, the system introduces innovative real-time notification approaches for surveillance systems such as WhatsApp messages and phone calls, in addition to SMS and emails. Thus, this system is reliable and meets the aim of a modern intelligent surveillance system by combining multiple approaches to detect intrusions and to inform users effectively.

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Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Camera Surveillance System with Effective Notification Features

Khodadin¹,

Pudaruth²

2020

IJCDS

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“…A dataset from CASAS project called Aruba [22] and Cairo are selected for this study since its labelled activities are good example to represent the daily life pattern of elderly residents in smart homes. The usage of the proposed unified model can be extended and implemented for different smart home applications such as energy-saving or security monitoring and threat detection [23].…”

Section: Introductionmentioning

confidence: 99%

Activities Recognition, Anomaly Detection and Next Activity Prediction Based on Neural Networks in Smart Homes

et al. 2022

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In this paper, we propose a unified deep learning model for monitoring elderly in execution of daily life activities such as eating, sleeping or taking medication. The proposed approach consists of three stages which are activity recognition, anomaly detection and next activity prediction. Such a system can provide useful information for the elderly, caregivers and medical teams to identify activities and generate preventive and corrective measures. In literature, these stages are discussed separately, however, in our approach, we make use of each stage to progress into the next stage. At first, activity recognition based on different extracted features is performed using a deep neural network (DNN), then an overcomplete-deep autoencoder (OCD-AE) is employed to separate the normal from anomalous activities. Finally, a cleaned sequence of consecutive activities is constructed and used by a long short-term memory (LSTM) algorithm to predict the next activity. Since the last two stages depend on the activity recognition stage, we propose to increase its accuracy by exploiting different extracted features. The performance of the proposed unified approach has been evaluated on real smart home datasets to demonstrate its ability to recognize activities, detect anomalies and predict the next activity.

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A Study on Tiredness Assessment by Using Eye Blink Detection

Islam¹,

Rahaman²,

Ahad³

2019

jkukm

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In this paper, the loss of attention of automotive drivers is studied by using eye blink detection. Facial landmark detection for detecting eye is explored. Afterward, eye blink is detected using Eye Aspect Ratio. By comparing the time of eye closure to a particular period, the driver’s tiredness is decided. The total number of eye blinks in a minute is counted to detect drowsiness. Calculation of total eye blinks in a minute for the driver is done, then compared it with a known standard value. If any of the above conditions fulfills, the system decides the driver is unconscious. A total of 120 samples were taken by placing the light source front, back, and side. There were 40 samples for each position of the light source. The maximum error rate occurred when the light source was placed back with a 15% error rate. The best scenario was 7.5% error rate where the light source was placed front side. The eye blinking process gave an average error of 11.67% depending on the various position of the light source. Another 120 samples were taken at a different time of the day for calculating total eye blink in a minute. The maximum number of blinks was in the morning with an average blink rate of 5.78 per minute, and the lowest number of blink rate was in midnight with 3.33% blink rate. The system performed satisfactorily and achieved the eye blink pattern with 92.7% accuracy.

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Suspicious Loitering Detection from Annotated CCTV Feed Using CEP Based Approach

Abstract: Smart Surveillance System is a critical system that enables automated detection of anomalous activities from live CCTV

Cited by 3 publications

References 18 publications

An Intelligent Camera Surveillance System with Effective Notification Features

An Intelligent Camera Surveillance System with Effective Notification Features

Activities Recognition, Anomaly Detection and Next Activity Prediction Based on Neural Networks in Smart Homes

A Study on Tiredness Assessment by Using Eye Blink Detection

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