Automated Attendance System Using Deep Learning
Introduction
Edge devices such as drones, IoT devices and cameras are becoming ubiquitous and playing an important role in our daily lives. For example, these devices are used more and more frequently for sensing tasks such as target tracking or object detection. These tasks require low latency and tend to be computationally and power consuming. Due to the real-time nature of the tasks and the limited power and computation capabilities of the edge devices, the edge devices offload the large amounts of collected sensory raw data to the cloud or to a centralized data-center, where it is processed and analyzed.
In this project we are interested in building an automated attendance prototype system. The system is constructed from two main hardware components, the camera (edge device) and the cloud. The camera takes photos of all the students entering the class and sends them to the cloud. In the cloud, these images are processed using state-of-the-art Convolutional Neural Networks (CNN). This design is optimal when the data transfer rate is high, and the raw data reaches the cloud in a timely manner. However, in real scenarios, the data is sent over bandwidth-constrained and fluctuating networks. Therefore, we are interested in analyzing the relationship between image compression (such as JPEG) and the accuracy of inference models.
Appendix
In this project, we also analyzed how different blur techniques affect the inference accuracy. As with JPEG compression, we used OpenCV library to blur the images. We used three types of blurring techniques: average, median and Gaussian. Average blurring is done by convolving the image with a kernel of size Failed to parse (syntax error): {\displaystyle k × k } . This type of blurring replaces the center element with the average of the pixel values under the kernel area. Median blurring is similar to average blurring, but, the central value is replaced with the median value under the kernel area. Gaussian blurring convolves the image with a Gaussian kernel. In this part we tried to convolve the image with different kernel sizes and compare between the different blurring techniques.
