Internet of things module accelerated dense deep learning for crime detection in surveillance systems

Abstract

The smart surveillance system is becoming a vital application in each streets or houses. Most of the streets are prone to several misbehavior conducts for instance theft in atm, robbery, fights etc. and hence it is necessary to detect and analyse the crime scenes for finding the suspects. However, most of the surveillance system suffers from poor detection of objects due to poor camera resolution, absence of light and other factors. In order to improve the detection of faces after detecting the objects using ResNet, it is necessary to adapt some advanced devices for image capturing and analyzing. In this paper, Internet of Things (IoT) based ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Module 2MP is used for image acquisition that capture better images from the scenes. The study uses dense convolutional network namely DenseNet to detect the faces present in the crime scenes after the object detection. The deep learning module is trained with selected crime scenes for training the classifier. The simulation is conducted further to validate the model with other variants of deep learning.