Exploring the Potential of Recurrence Quantification Analysis for Video Analysis and Motion Detection

The video monitoring of outdoor sites is a demanding task that is commonly tackled by having security guards look at arrays of CCTV monitors. Experience shows that this is largely ineffective, both as a detector and a deterrent. However, modern digital imaging systems can solve both these problems by maintaining constant vigilance 24 hours a day. These systems can be versatile and can operate in several different modes, video motion detection, video nonmotion detection and incident capture, thus providing a flexibility of application environment. By basing these systems on powerful PC technology the end user benefits from a large range of facilities at relatively low cost. In particular, it is possible to have low cost frame storage and high performance communications over telephones, ISDN or Ethernet. Image sequences both prior to and after an event can be stored and transmitted. Archiving and retrieval of events can be done efficiently through standard databases. However, in order that such systems be operationally viable it is essential that the detection algorithms be smart enough to reduce the number of false alarms to virtually zero. Most of the discussion concerns technology that is currently available and in everyday use: the author uses the ASTRAGUARD product as a specific example of such a system.

A survey conducted by The United States Department of Health and Human Services (2014), reported that 3.6 million children are victims of child abuse that occurred in the country. In fact, not only in U.S, but child abuse also occurred in around the world, especially in areas with high children population. Monitoring systems became an important and popular in the era of technological development. It serves as a deterrent to violent crime and prevent burglary. In this research project, we focus on child monitoring system using motion and authentication with Raspberry Pi. We use Raspberry Pi microcomputer with evolutionary prototype method in order to monitor the children with video streaming media and motion detection. Moreover, this prototype can help parents to monitor children which can be accessed easily by online through the website with authentication feature. The results of this research project showed that the prototype system using Raspberry Pi microcomputer can provide a sense of security and assist parents in monitoring a child. Parents able to monitor their children with real-time video streaming media and motion detection. Once the motion is detected, this system will identify with red-box. To make it easier to use, the system can be accessed online through the web that have authentication feature.

We consider the analysis of surveillance video footage containing occasional activities of potential interest interspersed with long periods of no motion. Such evidence is problematic for three reasons: firstly, it takes up a great deal of storage capacity with little evidential value; secondly, human review of such surveillance is extremely time-consuming and subject to errors due to fatigue; and thirdly, there is often a need to prove to the satisfaction of the Court that excised footage contains no images of evidential value. We are therefore concerned with objective, reliable detection of video motion to automate the extraction of activities of interest and to provide simple but reliable measurements to the court to prove that this is a complete record of all activities in the footage. Early results indicate that average luminance-based detection is particularly reliable, and we provide a comparison with other frame-difference techniques.

This article introduces motion detection and estimation of low-level-light video sequence, and, motion detection, motion estimation and variational problem. Low-level-light video sequence different form others, the time and space domain noise in the signal not only limit the lowest illuminance of the system but also make the image show random glitter. In this paper how to improve the signal-to-noise ratio (SNR) of low light level image is discussed too. The results show that models and estimation algorithms in low-level-light video sequence can lead to improve reliability and accuracy of the estimated motion.

Unmanned Aerial Vehicles (UAV), especially in the form of Micro Aerial Vehicles (MAV) are useful tools for reconnaissance, surveillance, and general situation assessment in safety, security, and rescue missions. Many UAV have meanwhile good autonomous flight capabilities, especially by tracking pre-planned routes via GPS or for station-keeping. Here it is shown how the video stream from an UAV can be analyzed to automatically detect motion in the scene while the vehicle is moving itself. Concretely, it is shown how a spectral image registration method, the improved Fourier Mellin Invariant method (iFMI), can be used for video stabilization and motion segmentation. The method is first analyzed with scenes containing artificial markers for ground truth evaluation. Furthermore, results from aerial video data from a quadcopter platform are presented.

Satellite videos are drawing attention of the frontiers in the core of video image processing. Satellite videos are in common used for weather forecast. The satellite weather videos are analyzed for various parameters in prediction of weather. The motion detection in these videos plays a vital role in the prediction of weather. By using the existing methodologies in the video motion detection, only the edges are detected as moving area and the others are left as such. There lies a need for the proposed methodology to detect the multiple motion detection for getting the satellite video processed with even more sensitivity and fidelity. This paper justifies the use of the proposed methodology using iterated training algorithm in the processing of satellite video downloaded from the NASA videos.

Several video detection systems that use a simple system of motion detection (if something moves, is generated an alarm) have been proposed, for this reason we trust part of the process to the human interpretation. Recent studies have demonstrated that a person is almost impossible to kindly watch a static scene in a monitor more than 20 minutes, doing that traditional systems of video monitoring as CCTV systems are little reliable, also, it is necessary to add numerous and annoying the false alarms generated by the few elimination of irrelevant information (color, light, shade, etc.) within the scene. The artificial vision nowadays allows having an automatic system of monitoring with the capacities to identify real threats and alert of security at the same time that they are happening. This paper presents a method of video motion detection that bases its use on an algorithm of discrimination able to eliminate the irrelevant information caused by natural effects (sun, moon, wind, etc.) or animals, maintaining the maximum of details on the image, allowing a better detection of motion through the distance of Hamming doubly justified, reducing in this way rate of false alarms, obtaining a method of motion detection automatic and reliable. In this paper is mentioned the comparison with other techniques, demonstrating itself that the proposed method gives better results. The obtained results show the basic characteristics of this method of detection.

Video Analytics is one of fastest thriving and crucial area of research. Video Analytics is process of automatically analyzing video to detect various activities. It is one of the most important areas for study today. Sometimes it is also called as Video Content Analysis as it is capable to analyze the video and detect the irregular events. In earlier age, video surveillance system mainly used to capture and monitor the events. In this system person has to keep watch on the camera scenes. Using Video analytics one can make the system more effective. Not only one can watch the video but also person detect the abnormal activity happened in the area. It decreases the workload of management staff. It preserves human's time and efforts. Video analytics finds application in many areas such as no man area, military, sports, civil etc. Many organizations use video analytics to detect the motion and capture the motion when organization is being closed. In this paper we were discussed about object motion detection and track the object when motion is detected. It is done with the help of video processing and Python and alerts with the help of Raspberry. Frame differencing is used for detecting movement of object and contour tracking algorithm to follow the movement of object. The code is simulated in Python.

Summary form only given. This paper discusses a means by which perimeter alarm systems can be combined to reduce the Nuisance Alarm Rate or False Alarm Rate (FAR) without compromising the Detection Rate (DR). Alarm systems operate on different principles and so have different strengths and weaknesses. Here we report work the Police Scientific Development Branch (PSDB) has conducted on combining complementary systems to reduce the FAR that would result from using either system on its own. We have ANDed a Video Motion Detection (VMD) System and a fence mounted microphonic Perimeter Intrusion Detection System. If each simultaneously goes into alarm, an operator is alerted. ANDing is not a new concept and has been used before, but not as far as we know to combine a Video Motion Detector (VMD) with a Fence Mounted System (FMS) at a real problem site. This paper reports and discusses the results from several operational trials of this combination of detectors. It gives a view on the practical value of ANDing such systems.

In recent years, digital videos are becoming available at an ever-increasing rate. It has never been easier for ordinary people to record, edit, deliver, and publish their own home-made digital videos over Internet. However, the increasing availability of digital video has not been accompanied by an increase in its accessibility. In other words, the abundance of video data makes it increasingly difficult for users to manage and navigate their video collections. In this paper, we first review the existing methodologies and technologies in video content analysis by addressing the trends and opportunities in consumer video content navigation and analysis. We then introduce a novel video content analysis framework using video time density function (VTDF) to tackle the problems in consumer video processing.

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.

Background subtraction is a widely used technique in motion detection. There are many challenges for motion detection in oceanic video like camera jitter, dynamic background and low visibility. If the background is dynamic or if the background changes overtime, a background update should be done in real time to precisely detect any kind of moving objects. In order to achieve an accurate underwater detection of motion in case of static camera with dynamic background in marine video, a novel detection scheme called sliding windowed fuzzy correlation analysis is proposed. The background modelling is based on sliding window technique, and the detection scheme is based on fuzzy correlation analysis. The window size is fixed to 12 in the algorithm to obtain better results and to reduce the latency in execution. The dataset considered here is ‘dataset on underwater change detection’ (Kaghyan and Sarukhanyan, Int J Inf Model Anal 1:146–156, 2012) that consists of five marine videos along with its ground truth. We qualitatively and quantitatively prove that the proposed method attains better motion detection as compared to other existing methods. The computational complexity involved is Intel Core i5 processor with MATLAB® software for simulation.KeywordsBackground subtractionCamera jitterDynamic backgroundSliding windowUnderwater motion detection

Motion detection deals with identifying movement of an object in a given video. Amongst the various techniques available to detect motion in video, background subtraction approach paves way for research activity. Hitherto, many algorithms are in-place to recognize motion based on background subtraction method but the need for fast and real-time motion detection is increasing due to its growing demand in video surveillance, intrusion detection system and other applications, motion detection serves as a beacon for the urge of new algorithms to classify the motion in a way better than the existing systems. This paper presents frame difference method to classify and detect motion in a video.

This paper proposes a cloud-oriented architecture for video analysis and motion detection. The core algorithm as been based on a typical computational intelligence method called Firefly Algorithm jointly with a Sobel filter in order to reduce the analysis complexity and the required computational effort. The developed system is completely self sufficient and highly scalable and expandable on demand. To achieve this result the developed architecture has beed accurately engineered by means of design patterns and structured as a layered application. Therefore the developed computational core is able to manage high level interfaces for the cloud environment as well as to take advantage of hardware level optimizations in order to maximize its performance and make it suitable for real time analysis of continuous video streams coming from multiple sources.

With the discovery of graphene, people have discovered that two-dimensional materials have unique properties and significance. Therefore, two-dimensional material-based photodetectors are also very valuable for research. This article first discusses the excellent properties of photodetectors based on two-dimensional materials, including high sensitivity, high modulation frequency, and wide spectral response capability. Then, recent research results on two-dimensional SnSe2/GaP type-II heterostructure photodetectors and photodetectors utilizing two-dimensional WSe2 were introduced. Additional representative studies, including two-dimensional layered materials-based valanche photodetectors and two-dimensional tin (II) sulfide (SnS) nanoflakes-based photodetectors, were also shown. The prospects and challenges of photodetectors based on two-dimensional materials were also discussed at the end of the article. They can be applied in fields such as video imaging, gas sensing, biological imaging, safety, night vision, optical communication, and motion detection. The issues of device stability, mass production, cost, and uniformity need to be addressed and find their more ideal commercial applications.