Systematic Study of Video Mining with Its Applications

Effective methods for video indexing will be more valuable as digital video data continues to grow. It has been years since we’ve seen this level of new multimedia research. The content analysis aims to create high-level descriptions and annotations by treating language and facts as data. Data mining is a technique that seeks out previously unknown facts and patterns in large datasets. A video can include several different kinds of data, such as images, visuals, audio, text, and additional metadata. Thanks to its broad application in various disciplines, like security, education, medicine, research, sports, and entertainment, it is often used differently. Data mining aims to discover and articulate exciting patterns that are hidden in a lot of video footage. While video mining is still in its infancy, data mining is more mature. A considerable amount of research must be done to turn the mined video into usable content

Semantic Video Mining for Accident Detection

This paper depicts the efficient use of CCTV for traffic monitoring and accident detection. The system which is designed has the capability to classify the accident and can give alerts when necessary. Nowadays we have CCTVs on most of the roads, but its capabilities are being underused. There also doesn’t exist an efficient system to detect and classify accidents in real time. So many deaths occur because of undetected accidents. It is difficult to detect accidents in remote places and at night. The proposed system can identify and classify accidents as major and minor. It can automatically alert the authorities if it deals with a major accident. Using this system the response time on accident can be decreased by processing the visuals of CCTV. In this system different image processing and machine learning techniques are used. The dataset for training is extracted from the visuals of already occurred accidents. Accidents mainly occur because of careless driving, alcohol consumption and over speeding. Another main cause of death due to accidents are the delay in reporting accidents since there doesn’t exist any automated systems. Accidents are mainly reported by the public or by traffic authorities. We can save many lives by detecting and reporting the accident quickly. In this system live video is captured from the CCTV’s and it is processed to detect accidents. In this system the YOLOV3 algorithm is used for object detection. Nowadays traffic monitoring has a greater significance. CCTV’s can be used to detect accidents since it is present in most of the roads. It is only used for traffic monitoring. Normally accidents can be classified as two classes major and minor. The proposed system is able to classify the accident as major or minor by object detection and tracking methodologies. Every accident doesn’t need emergency support. Only major accidents must be handled quickly. The proposed system captures the video and undergo object detection algorithms to identify the different objects like vehicles and people. After the detection phase

Estimation of Wind Turbine Angular Velocity Remotely Found on Video Mining and Convolutional Neural Network

Today, energy issues are more important than ever. Because of the importance of environmental concerns, clean and renewable energies such as wind power have been most welcomed globally, especially in developing countries. Worldwide development of these technologies leads to the use of intelligent systems for monitoring and maintenance purposes. Besides, deep learning as a new area of machine learning is sharply developing. Its strong performance in computer vision problems has conducted us to provide a high accuracy intelligent machine vision system based on deep learning to estimate the wind turbine angular velocity, remotely. This velocity along with other information such as pitch angle and yaw angle can be used to estimate the wind farm energy production. For this purpose, we have used SSD (Single Shot Multi-Box Detector) object detection algorithm and some specific classification methods based on DenseNet, SqueezeNet, ResNet50, and InceptionV3 models. The results indicate that the proposed system can estimate rotational speed with about 99.05 % accuracy.