WildTrackAI – Applies Computer Vision to Animal Footprint Classification


  • This Project propose animal identification system that employs image processing techniques.
  • Firstly, the collected footprint images are pre-processed.
  •  Images are converted into gray-scale and boundaries of image are determined using canny algorithm.
  • Further, footprint images are segmented. Gabour filter are used to extract features of segmented image.
  • After feature extraction, features are reduced based on unsupervised model.
  •  Then reduced feature vectors are inputted into the classification model. Probabilistic Neural Network (PNN) is used for classification and identifying the animal class.


  • Several techniques currently exist to study the behavior of animals.
  • Mostly used technique was Sound Classification or image classification of the particular animals
  • But the Problem was most of the time human unable to capture the sound and photo of the particular animal due to unavailability.


  • Humans have difficulty in accurately carrying out this task because not every footprint left by an animal is a perfect one.
  • Footprints are usually smudged or only partially visible.
  • An animal could be identified by humans who wouldn’t contain any prior knowledge of animal footprints.


  • The image processing techniques have been established to optimize the footprints, input image is converted into greyscale, Edge detection on the image.
  • All the images in the dataset are read, processed, and feature extracted, raw data is loaded for classification of input image.
  • This project proposed a Gabour filter response, for extracting texture features and preserves texture features of an image in frequencies.
  • Selective scale and orientation filter is applied on input image to acquire texture features.
  • And Segmentation requires separating the image from the background for efficient classification.
  • Next step is extracting templates of the footprints.
  • In template matching process along with template updating is specified.



Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements:

  • Hard Disk: Greater than 500 GB
  • RAM: Greater than 4 GB
  • Processor: I3 and Above

 Including Packages

* Base Paper

* Complete Source Code

* Complete Documentation

* Complete Presentation Slides

* Flow Diagram

* Database File

* Screenshots

* Execution Procedure

* Readme File

* Addons

* Video Tutorials

WildTrackAI – Applies Computer Vision to Animal Footprint Classification, The Footprint Identification Technique,  Animal Identification Using Footprints  machine learning, Animal identification based on footprint recognition image proccessing, Animal classification using facial images with score‐level fusion, Identifying endangered species from footprints,  Automation of Animal Classification using Deep Learning