Abstract
Smart surveillance system is designed and developed to mitigate the occurrence of crime scenarios. Traditional image processing methods and deep learning approaches are used to identify the knife from camera feed. On identification of knife, the identity of person holding the knife is obtained using SSD ResNet CNN model. Also, an awareness alarm is generated by the system to caution the people in the surroundings. Experimental investigation clearly shows that the method of fine-tuned Xception deep learning model based on Separable Convolutional Neural Network (SCNN) with Logistic Regression (LR) classifier resulted in highest accuracy of 97.91% and precision rate of 0.98. Face detection is employed using a conditional face detection model based on SSD ResNet. The result obtained using deep learning approach is high compared to that of traditional image processing method. Real time implementation result shows that the model effectively detects the knife and identifies the person holding knife.
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Acknowledgement
The authors would like to thank NVIDIA for providing NVIDIA TITAN X GPU under University Research Programme.
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Vaitheeshwari, R., Sathiesh Kumar, V., Anubha Pearline, S. (2020). Design and Implementation of Human Safeguard Measure Using Separable Convolutional Neural Network Approach. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_29
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