Detecting objects and people and tracking movements in a video using tensorflow and deeplearning
Jemai Bornia, Ali Frihida, Christophe Claramunt · 2020
With the advent of the digital age and more specifically videos, a huge amount of data is produced every day such as television archiving, video surveillance, etc. Faced with the need to keep control over this content, to be able to analyze it, classify it and many other applications, the need for algorithms capable of performing this task efficiently and quickly is undeniable. The proposed approach permits the analysis of video sequences using deep learning and TensorFlow technologies. The proposed approach splits video in set of images, detects objects/entities present in these images and stores their descriptions into a standard XML file. With an algorithm we developed, we're able track motion that animated entities in the video sequences.