11/4/2022 0 Comments Cctv installation pdf malayalamFirst of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. In this paper, we proposed a fast image retrieval method which designed for big data. So we should transform the unstructured image data source into a form that can be analyzed. #Cctv installation pdf malayalam how toThe value density of information utilization in big data is very low, and how to extract useful information quickly is very important. In the field of big data applications, image information is widely used. Experiments illustrated the applicability of the method. This fact is used to model the probability of occurrence of fire as a function of the position. For edited newscast videos, the fire region is usually located in the center of the frames. In addition, a priori knowledge of fire events captured in videos is used to significantly improve the classification results. The behavioral change of each one of these features is evaluated, and the results are then combined according to the Bayes classifier for robust fire recognition. Because of flickering and random characteristics of fire, these features are powerful discriminants. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. The proposed method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. In the latter case, there are large variations in fire and background characteristics depending on the video instance. In contrast, the proposed method can be applied not only to surveillance but also to automatic video classification for retrieval of fire catastrophes in databases of newscast content. Computer vision-based fire detection algorithms are usually applied in closed-circuit television surveillance scenarios with controlled background. In this paper, we propose and analyze a new method for identifying fire in videos. Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events such as suspicious activity detection and fire detection in surveillance applications.Īutomated fire detection is an active research topic in computer vision. Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms. To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices which increase its suitability for IoT systems. As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses. When an event is detected from keyframes, an alert is sent to the concerned authority autonomously. Firstly, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors. This paper proposes a secure surveillance framework for IoT systems by intelligent integration of video summarization and image encryption.
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