Amid training, the main focus was on stacking the face cover detection dataset from disk, training a model (utilizing Keras/TensorFlow) on this dataset, and after that serializing the face mask detector to disk. To get better results in a short time the approach Transfer Learning is used and the MobileNetV2 architecture is trained on the weights of ImageNet. Google images are scraped using the selenium package and Chrome Driver extension. Dataset used in this module is from Google Images and Kaggle. Therefore, the output of this module will help in determining if the theft is real or false. Thieves usually wear masks to hide their identity while attempting theft. Extra strategies that ought to be received in order to identify ceased objects for the victory of the next level are computationally complex and cannot be utilized in real-time without specialized equipment. But this strategy is exceedingly versatile to energetic scene changes in any case, it by and large falls flat in identifying entirely significant pixels of a few sorts of moving objects. This method is straightforward and simple to execute, conjointly it is comparable to the background subtraction. The frame differencing strategy employments the two or three adjacent frames based on time series picture to subtract and gets diverse pictures, its working is exceptionally comparative to background subtraction, after the subtraction of the picture it gives moving target data through the edge esteem. and got the most excellent comes about with frame differencing. To identify the motion different strategies like frame differencing, background subtraction, optical flow, etc. After detecting the human being in the frame we now can detect the motion and so we used python library "OpenCV". We tried transfer learning for that we used the YOLOv4 neural network and extracted the weights just before the last two layers of the network and then used those pre-trained weights to train the model which can detect the human being in the image and using the transfer learning technique accuracy got improved. Convolution can be used to achieve the blurring, sharpening, edge detection, noise reduction, which is not easily achieved by other methods.Īfter getting quite normal results from the model trained on a dataset with 2000 images with a human being and 2000 images without a human being. To detect the motion we firstly detected if any human being is present in the frame or not and to do so we used CNN. day and night) and it will totally depend on the user which mode is required at the moment. There are a total of six levels of surveillance and the system consists of two modes (i.e. The system consists of several levels of surveillance at each level the activity in each frame of the video will be monitored thoroughly using ML models, which are solely trained to perform their specific job. This paper aims to design a theft detection and monitoring system, which would be capable to detect theft using a motion-sensing camera using ML and alarm the owner with an alert message along with the captured image of that instance of motion. Machine Learning (ML) techniques prove to be fruitful in developing efficient surveillance systems. Therefore, there is a need to develop a more deterrent surveillance system, which is convenient to use, free from false alarms, minimize human interference, and cost-effective. Increasing theft rates cause people to suffer both financially and emotionally. According to the National Crime Records Bureau (NCRB), ~80% of the criminal cases are related to theft as shown in figure. Theft is the most common crime committed across the world. You have to work on that!! Intruder without mask.
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