Analyzing video produced by a stationary surveillance camera (original) (raw)
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With increased technology and population, surveillance is becoming key area in research. The best utilization of technology for the surveillance is the focus area today. The focus of the project would be to automate the detection of actions performed in front of camera. Monitoring the events on camera manually will not be possible. Even if the event had happened in the past, searching the same event in the video manually would cost a lot time. There is scope for automation which should detect those events and provide detailed textual result which can be processed easily. This project also considers effective way to store the video surveillance data consuming as small memory as possible along with smart index information. Video surveillance is an important security asset to control theft, traffic monitoring, banks, department stores, highways, crowded public places and borders.
The OBSERVER: An Intelligent and Automated Video Surveillance System
Image Analysis and Recognition, 2006
In this work we present a new approach to learn, detect and predict unusual and abnormal behaviors of people, groups and vehicles in real-time. The proposed OBSERVER video surveillance system acquires images from a stationary color video camera and applies state-of-the-art algorithms to segment and track moving objects. The segmentation is based in a background subtraction algorithm with cast shadows, highlights and ghost's detection and removal.
Sixth Indian Conference on Computer Vision, Graphics …, 2008
Video is a powerful tool to show various activities but generally we use still images to show a condensed video, which is problematic in viewing and comprehending. Thus, there is a need for a summarized surveillance video. A fundamental goal of any video summarization or synopsis technique with reference to a surveillance video is to reduce the Spatio-temporal redundancy. The activity in any surveillance video is very less as compared to the total length of the video. The spatial redundancy is removed by showing two activities that happened in different frames at different spatial locations in a single frame. Temporal redundancy is removed by detecting the frames having low activity and then deleting those frames. We then generate a stroboscopic video, which traces path of the extracted object. Lastly, we introduce a media Player, which indexes the video synopsis to the original video demonstrating how the video synopsis can be used as an effective tool.
Surveillance Video Summarization Based on Object Motion Pattern Analysis
2015
This paper proposes a novel fusion method for summarization of surveillance videos based on motion pattern analysis of each detected object. The method allows to summarize long videos into a single index frame. Generated index frame is shortest possible summary of given video which also serves as a bookmark for a long video. Experimental results demonstrate its effectiveness.
Real-Time video-surveillance by an Active Camera
In this paper, we propose a real-time video-surveillance system for image sequences acquired by a moving camera. The system is able to compensate the background motion and to detect mobile objects in the scene. Background compensation is obtained by assuming a simple translation of the whole background from the previous to the actual frame. Dominant translation is computed on the basis of the tracker proposed by Shi-Tomasi and Tomasi-Kanade. Features to be tracked are selected according to a new intrinsic optimality criterion. Badly tracked features are rejected on the basis of a statistical test. The current frame and the related background, after compensation, are processed by a change detection method in order to obtain a binary image of moving points.Results are presented in the contest of a visual-based system for outdoor environments.
Development of a flexible video analysis system for motion detection and alarm generation
2010
This paper aims at developing a flexible video analysis system that can be used in wide range of video surveillance applications as well as to detect the human being. The developed system is called here as Smart Video Analysis System. This SVAS is able to detect and track interested objects. It can also detect people and recognize their activities in an application environment, such as in a room, supermarket, car, or security checkpoint. Designing a real-time video analysis system is a complex task, as many factors including processing speed, system cost, accuracy, and robustness, need to be carefully balanced. This research has focused these factors at two levels, algorithm level and software level. Background elimination algorithm is proposed in this paper to enhance the performance of Smart Camera systems in changing background and varying lighting condition environment. Finally, the software implementation of the Smart Camera Analysis Systems is applied to detect motion and then to trigger alarm for the security purposes. The system will trigger alarm once the motion is detected and when it exceeds the desire threshold value.
Clustered Synopsis of Surveillance Video
Advanced Video and Signal Based Surveillance, 2009
Millions of surveillance cameras record video around the clock, producing huge video archives. Even when a video archive is known to include critical activities, finding them is like finding a needle in a haystack, making the archive almost worthless. Two main approaches were proposed to address this problem: action recognition and video summarization. Methods for automatic detection of activities still face problems in many scenarios. The video synopsis approach to video summarization is very effective, but may produce confusing summaries by the simultaneous display of multiple activities.
Tracking and video surveillance activity analysis
Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia - GRAPHITE '06, 2006
The explosion in the number of cameras surveilling the environment in recent years is generating a need for systems capable of analysing video streams for important events. This paper outlines a system for detecting noteworthy behaviours (from a security or surveillance perspective) which does not involve the enumeration of the event sequences of all possible activities of interest. Instead the focus is on calculating a measure of the abnormality of the action taking place. This raises the need for a low complexity tracking algorithm robust to the noise artefacts present in video surveillance systems. The tracking technique described herein achieves this goal by using a future history buer of images and so delaying the classication and tracking of objects by the time quantum which is the buer size. This allows disambiguation of noise blobs and facilitates classication in the case of occlusions and disappearance of people due to lighting, failures in the background model etc.
This paper is review of many existing video surveillance systems. With the growing quantity of security video, it becomes vital that video surveillance system be able to support security personnel in monitoring and tracking activities. The aim of the surveillance applications is to detect, track and classify targets. In this paper is described object modelling, activity analysis and change detection. In this paper we will also describe a design of our video surveillance system.