Detection of Soccer Players from Thermal Images using Monk AI

Detection of Soccer Players from Thermal Images using Monk AI

In this tutorial, we will be making an object detection application using the thermal image dataset from an indoor soccer field. Using this application we will be able to track the number of players present on the ground at a particular time, this application can therefore be used in target tracking activities.

Introduction

In this tutorial, we will be making an object detection application using the thermal image dataset from an indoor soccer field. Using this application we will be able to track the number of players present on the ground at a particular time, this application can therefore be used in target tracking activities. This would help us in tracking multiple people, especially in activities where people move quickly and erratically and wear similar uniforms. Monk’s object detection toolkit allows us to deploy our model using low-code syntax, and one-line installation of different deep learning pipelines makes our work easier.

Create real-world Object Detection applications using Monk

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Object Detection in Bad Light

About Dataset

The thermal soccer dataset is available on Kaggle, this dataset is captured using thermal cameras which ensures better segmentation and ensures the privacy of people in public facilities.

This dataset contains four 30-seconds video sequences of 8 people playing soccer in an indoor arena. The video is captured using thermal cameras of type AXIS Q1922 with a resolution of 640480 pixels and 25 fps. The three images are stitched to one image of 1920*480 pixels.

The videos are manually annotated for tracking.

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