Animal Footprint Detection

Abstract

Animal footprint detection is a powerful tool in
wildlife monitoring, ecological research, and
conservation efforts. By identifying and analyzing
footprints left by animals, researchers can gain
valuable insights into species presence, movement
patterns, population size, and behavior without the
need for direct observation. Traditional methods of
footprint analysis are often time-consuming and
prone to human error. With advancements in
computer vision and machine learning, automated
systems can now detect, classify, and track animal
footprints from images captured in natural
environments, enabling faster and more accurate
data collection.
This project aims to develop an automated animal
footprint detection system using image processing
techniques and deep learning models. The system
will preprocess input images to enhance footprint
visibility, then apply trained neural networks to
recognize patterns specific to various animal
species. Such a system has broad applications,
including poaching prevention, biodiversity
assessment, and habitat protection. By leveraging
technology, we can support more efficient wildlife
tracking and contribute to the sustainable
management of ecosystems.