Since few years there is tremendous growth in the research of surveillance system especially in computer vision based surveillance system. This paper proposes a monitoring system which includes motion detection algorithm is implemented along with some alarming features. The system proposed in this paper is IOT based image processing unit with internet connectivity so that data can be transferred to any remote server for example Cloud. The motion detection feature of a system provides necessary isolation of most significant video clips that eliminates time consuming reviewing process required in normal video surveillance system. The main idea behind this system is to detect the presence of any moving object especially a human and then start recording a video from the motion is detected. The video clip can be stored locally as well as uploaded to cloud server so that it can be viewed from anywhere in the world and necessary alarms then generated by the system. There are several micro controllers are available that can be interfaced along with digital camera to achieve this goal.

Intelligent embedded video monitoring system for home surveillance

In recent years, lots of research have been done in computer vision domain. Video surveillance in real-time scenario, especially for humans, like tracking and behavior analysis is one of the most active research topics in computer vision and artificial intelligence in present situation. The main focus is providing the low-cost and efficient video surveillance system for home application and can have wide scope in other areas such as elevator monitoring and server room monitoring. As the traditional surveillance system requires huge storage capacity and consumes lot of network bandwidth, hence it becomes necessary to provide solution for such design issues. This paper presents the implementation of motion detection algorithm for live camera streaming and thus allows analyzing incoming image stream, and recognizing any movement occurs in the area and if so then triggers video recorder to save the information-contained video clip.

Home Surveillance System Using Internet of Things

Quality assurance of drinking water is vital in the current world. This research used fish to monitor and forewarn water quality in real time. This research adopted a Gaussian mixture model (GMM) to conduct background modeling, extract moving foregrounds, and automatically judge, screen, and dispose of various contours in the binary images through image preprocessing and morphological processing, and outlined the changing curves of indicators using a computer. The experimental results demonstrated that the above method was successful. This system was able to monitor the active state of a fish in real-time and accurately raise the alarm in the case of an abnormal condition. Based on this research, it would be easy to measure other indicators and develop extended functions that would be beneficial for follow up studies.

Water Monitoring System Based on Recognition of Fish Behavior

With the increase in video surveillance data, techniques such as video synopsis are being used to construct small videos for analysis, thereby saving storage resources. The video synopsis framework applies in real-time environments, allowing for the creation of synopsis between multiple and single-view cameras; the same framework encompasses optimization, extraction, and object detection algorithms. Contemporary state-of-the-art synopsis frameworks are suitable only for particular scenarios. This paper aims to review the traditional state-of-the-art video synopsis techniques and understand the different methods incorporated in the methodology. A comprehensive review provides analysis of varying video synopsis frameworks and their components, along with insightful evidence for classifying these techniques. We primarily investigate studies based on single-view and multiview cameras, providing a synopsis and taxonomy based on their characteristics, then identifying and briefly discussing the most commonly used datasets and evaluation metrics. At each stage of the synopsis framework, we present new trends and open challenges based on the obtained insights. Finally, we evaluate the different components such as object detection, tracking, optimization, and stitching techniques on a publicly available dataset and identify the lacuna among the different algorithms based on experimental results.

/pdf/video-synopsis-algorithms-and-framework-a-survey-and-123fmmnz.pdf

Video Synopsis Algorithms and Framework: A Survey and Comparative Evaluation

With the advances in video technology, the advent of spherical video (360° video) recorded using an omnidirectional camera offers a limitless field-of-view (FoV) to the viewers. However, they suffer from the fear of missing out (FOMO) because they can only see a particular FoV at a time. Reviewing a long recorded surveillance video i.e., 24 hours a day is a time-consuming process due to temporal and spatial redundancy. A solution to this problem is to compactly represent the video synopsis by shifting the objects along the time domain. Using a multi-camera setup for surveillance creates blind spots. This problem is solved by using a spherical camera. Therefore, in this paper, we focus on creating and visualizing the video synopsis recorded by the spherical camera. The optimization algorithm plays a key role in condensing the recorded video. Hence, a novel spherical video synopsis optimization framework has been introduced to generate compact videos that eliminate FOMO. The synopsis is generated by shifting objects on the temporal axis and displays them simultaneously by optimizing multiple constraints. It minimizes activity loss, virtual collisions, temporal inconsistencies, and synopsis video length by preserving interactions between objects. The proposed multiobjective optimization includes a new constraint to restrict the number of objects displayed per frame due to the limitation of the human visual system. Direction-based visualization methods have been proposed to improve the viewer’s experience without FOMO. Comparative performance of the proposed framework using the latest metaheuristic optimization algorithms with existing video synopsis optimization algorithms is performed. It is found that chronological disorder ratio and overall virtual collision are minimized effectively through the recent metaheuristics optimization algorithms compared to the related works on video synopsis.

Spherical video synopsis generation and visualization framework

Intelligent video surveillance systems deals with the monitoring of the real-time environment. It monitors the transient and persistent objects within a specific environment. This is not only designed for security systems and can also be applied for external environmental video surveillance. The basic background subtraction algorithm is used for the detection of moving object. A self-adapting, automatic updating background model is trained to adapt the slow and slight changes of the environment. The foreground object is detected when the subtraction of the current image and the background, which is already trained attains a threshold, the foreground moving object is considered as to be in current view. The mobile phone automatically notifies the control unit through SMS(Short Message Service)or by phone call. Background subtraction technique basically works by feature analysis, pixel differences and so on. Here for feature analysis k-nearest neighbour algorithm (k-nn) is implemented. This proposed system requires little memory and less storage space than the previous. This can be used in implementing mobile based security monitoring system.

/pdf/foreground-object-motion-detection-by-background-subtraction-2z6magetrf.pdf

Foreground Object Motion Detection by Background Subtraction and Signalling Using GSM

Foreground object motion detection by background subtraction and signalling using GSM

PanoSyn: immersive video synopsis for spherical surveillance video

Video synopsis is an effective way to easily summarize long-recorded surveillance videos. The omnidirectional view allows the observer to select the desired fields of view (FoV) from the different FoV available for spherical surveillance video. By choosing to watch one portion, the observer misses out on the events occurring somewhere else in the spherical scene. This causes the observer to experience fear of missing out (FOMO). Hence, a novel personalized video synopsis approach for the generation of non-spherical videos has been introduced to address this issue. It also includes an action recognition module that makes it easy to display necessary actions by prioritizing them. This work minimizes and maximizes multiple goals such as loss of activity, collision, temporal consistency, length, show, and important action cost respectively. The performance of the proposed framework is evaluated through extensive simulation and compared with the state-of-art video synopsis optimization algorithms. Experimental results suggest that some constraints are better optimized by using the latest metaheuristic optimization algorithms to generate compact personalized synopsis videos from spherical surveillance videos. 

S. Priyadharshini

Papers

Foreground Object Motion Detection by Background Subtraction and Signalling Using GSM

Foreground object motion detection by background subtraction and signalling using GSM

PanoSyn: immersive video synopsis for spherical surveillance video

PanoSyn: immersive video synopsis for spherical surveillance video

A Personalized Video Synopsis Framework for Spherical Surveillance Video