42,696 research outputs found

    Efficient human motion detection with adaptive background for vision-based security system

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    Motion detection is very important in video surveillance system especially for video compression, human detection, and behaviour analysis. Various approaches have been used for detecting motion in a continuous video stream but for real-time video surveillance system; we need a motion detection that can provide accurate detection even in non-static background regardless of surroundings (outdoor or indoor), object speed and size, robust to camera noisy pixels or sudden change in light intensity. This is very important to ensure that the security of a monitored parameter or area is not compromised. In this paper, we propose a method for human motion detection that employs adaptive background subtraction, camera noise reduction and white pixel count threshold for real-time video streams

    Mobile surveillance system for UiTM (Terengganu) using motion detection mechanism / Mohd Ridhwan Mohamed Sari

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    The surveillance system provides the capability of image streaming and motion detection using high technology camera. In a general surveillance system, video stream are sent to the control center and operator monitor the video stream. However, there are some problems in this surveillance system. The operator that monitors the video only can be in a fixed location. It can cause an effective result of the surveillance system. By using this manual surveillance system, the operators that monitor the video need to monitor without blinking their eyes in order to make sure all the movements that been captured by the cameras are watched. To overcome that problem, some solution has been discovered. It is monitor the surveillance video using mobile surveillance system by mobile device and implementation of motion detection mechanism. By using mobile device as front-end of distributed surveillance system, operator can monitor the camera surveillance anytime and everywhere using mobile device. Motion detection mechanism can provide an alert where there are movements that have been captured by the camera in this mobile surveillance system. The server then automatically send alert to the user in order to pay attention that the motion is detected. This mobile surveillance system using motion detection mechanism (M2SMoDeM) has been tested at the Post B UiTM (Terengganu). The result of this proposed system is evaluated based on the usability and functionality by set of questionnaire. The results have shown that the overall evaluations gave very positive feedback about the M2SMoDeM. M2SMoDeM had achieved the objectives, but there still some areas that can be studied for future research directions. This project needs to improved version in stream via RTSP connection. Besides, it needs to improved the motion detection mechanism with intelligently in detect and manage the alerting system when the suspicious motion is detected. In a nutshell, the security of mobile surveillance system is more reliable than manual surveillance system that using more human capability to monitor the video and detect any suspicious movement

    A Design Methodology of an Embedded Motion-Detecting Video Surveillance System

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    Malaysia urbanization rate has been growing fast due to rapid development and modernization (Economy Planning Unit, 2015) (World Bank, 2015). With the fast growing urban population, the property crimes associated also rises at an alarming rate (United Nations Human Settlements Program, 2007) ( Mohit Mohammad Abdul, Elsawahli H. Mohamed Hassan, 2015) (Ministry of Home Affairs, 2015). Video surveillance system is one of the trusted and efficient security systems to protect people and property against criminal (Varij Ken, 2015). Three problems and issues regarding current video surveillance system are functionality, flexibility and efficiency. The aim of this project is to design and develop an embedded motion detecting video surveillance system and to produce a functioning prototype as the final result to solve these problems. In order to achieve the aim of this project, three objectives are set to be attained. These objectives are to design and develop a motion detection algorithm based on OpenCV library functions using camera as sensor, to process and execute the algorithm using an embedded micro-computer and to compare its processing performance with a PC and to enable wireless user alert using LAN and internet connection. The first objective is achieved as the motion detection algorithm designed and developed based on background subtraction method using camera sensor and openCV library function is proven to functioning well in detecting for motion changes. The algorithm performs successfully both in BeagleBone Black module and PC and is able to deliver outputs required for embedded motion detection video surveillance system. The second objective is achieved as BeagleBone Black (BBB) module, a micro-computer embedded system is used as main processor for the embedded motion-detecting video surveillance system. The BBB module is able to process and execute the motion detection algorithm designed with image processing functions from OpenCV library. The third objective is achieved as embedded motion-detecting video surveillance system is equipped with wireless user alert function using local area network and internet connection. The system is able to send real-time alert to the user via email attached with the image captured and detected as a threat by the syste

    Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges

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    Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed

    IVEE: Interesting video event extraction

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    The Video Exploitation and Novelty Understanding in Streams (VENUS) system is a complete software solution for video surveillance, which consists of motion detection, motion tracking, novel event detection, missed expected event detection, object recognition, scene description, and database mining. This research focuses on the assignment of degrees of interest for events in motion and at rest for the VENUS system. The Interesting Video Event Extraction (IVEE) system is the second module in the processing pipeline of the VENUS system. The novel features of the IVEE sysem include the ability to assign a degree of interest to an event, to develop a representation of the weekly activities from the input video stream, and to detect when an expected event did not occur. The IVEE system maintains independence through self-learning and without the aid of human intervention to understand the difference between normal and abnormal behaviors

    Features-based moving objects tracking for smart video surveillances: A review

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    Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking, motion analysis, and require fusion of information from the camera networks. This paper reviews recent techniques used by researchers for detection of moving object detection and tracking in order to solve many surveillance problems. The features and algorithms used for modelling the object appearance and tracking multiple objects in outdoor and indoor environment are also reviewed in this paper. This paper summarizes the recent works done by previous researchers in moving objects tracking for single camera view and multiple cameras views. Nevertheless, despite of the recent progress in surveillance technologies, there still are challenges that need to be solved before the system can come out with a reliable automated video surveillance

    Motion Detection and Face Recognition for CCTV Surveillance System

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    Closed Circuit Television (CCTV) is currently used in daily life for a variety purpose. Development of the use of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognation in CCTV video is done to be a base for decision making to produce automated, effective and efficient integrated system. This CCTV video processing provides three outputs, a motion detection information, a face detection information and a face identification information. Accumulative Differences Images (ADI) used  for motion detection, and Haar Classifiers Cascade used  for facial segmentation. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). The features was trained by Counter-Propagation Network (CPN). Offline tests performed on 45 CCTV video. The test results obtained a motion detection success rate of 92,655%, a face detection success rate of 76%, and a face detection success rate of 60%. The results concluded that the process of faces identification through CCTV video with natural background have not been able to obtain optimal results. The motion detection process is ideal to be applied to real-time conditions. But in combination with face recognition process, there is a significant delay time

    Developing a robust framework to reduce the size of a recorded video surveillance systems

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    Most of the video surveillance strategies take a significant amount of space for storage as surveillance camera's unexceptionally recorded everything during camera – on time. Whereby, it leads to consuming the storage capacity of the device of the system. In fact, many algorithms have been proposed solving in the dilemma to object recognition and compress the video to reduce the size whenever it save's data. Nevertheless, the technology deprived efficient methods to reducing the storage of space for consummation. The Idea of this paper is to propose a framework on how to possibly can be reduce the size of a recorded video of the surveillance system via recording only the part of the video that contains the motion, and ignore the other parts based on the motion detection. The result shows that the framework give an outstanding results on the uncompressed surveillance video recorded from a single fixed camera. The proposed framework enables to save 30% more of playback time and can provide more than 50% of storage of space saving
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