Application of Deep Learning Techniques to Depth Images for Person Tracking and Detection
Velton Cardoso Pires, Eduardo Silva Palmeira, Felipe Antunes Santos · 2023
Nowadays, using neural networks for image processing and tracking of individuals/objects is a highly popular subject that can be applied to various real-world issues. However, for such cases, the image often needs to possess good quality and exhibit distinct features to aid in object detection, posing challenges in environments with low or no illumination. In our work, we present a comparative study on the performance of leading convolutional neural networks for the detection and tracking of individuals through depth color images generated by infrared sensors. Additionally, we aim to demonstrate the usability of the YOLO (You Only Look Once) architecture as an alternative for identifying objects in images generated by sensors that do not rely on illumination. Experimental results showcase that the approach using YOLO Tiny improves accuracy by approximately 9% and processes around 8 times more frames per second (FPS).