One of the important applications in the smart surveillance system is to identify unknown suspicious persons. Instead of manually and tediously monitoring the cameras continuously, this system can be used to identify and recognize suspected individuals and consequently send a warning message on the occurrence of such recognition. In this work, we propose a novel methodology, in which the system is connected to a database containing images of the Aadhar cards of the malfunctioning individuals. Since SQL database cannot directly store images, the Aadhar card pictures will have to be stored in a NoSQL database. The proposed system was built utilizing the Python language and the PyCharm IDE. The input video stream is processed frame by frame using the OpenCV library. The system will process each frame to check for the presence of a face. If a face is found, it matches the detected face with each of the faces present in the Aadhar card photos stored in the connected database. This process is carried out using a face recognition algorithm. If a match is found, the system uses OCR to isolate and recognize the text present in the image in order to get the name and Aadhar card number of the offender, and subsequently an alert will be displayed to the corresponding moderators with the name and Aadhar card number of that individual, in order to facilitate the necessary action.
The concept of Convolution Neural Networks (CNN) has been becoming highly significant in computer-vision-based applications. Their applications in disaster management, like fire detection, will definitely improve the social and ecological environment. Most of the existing fire detection systems fail to detect fire in certain environments like smoke, fog and so on. In this paper, we propose a Squeeze-Net framework-based CNN for detection of fire, localization and understanding the scene of fire. The method uses smaller convolution layers with no dense layers, thus minimizing the computational power. Experimental results suggest improved performance in terms of accuracy and loss parameters for both known and unpredictable image settings. Despite the low computational power, the method provides more accuracy than state-of-art techniques.
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