# Object tracking in video signals using Compressive Sensing

## TL;DR

This paper explores reducing pixel count in video signals via Compressive Sensing to efficiently track moving objects, demonstrating that lower pixel counts still preserve object trajectories.

## Contribution

It introduces a method for object tracking in videos with significantly reduced pixel data using Compressive Sensing, maintaining trajectory accuracy.

## Key findings

- Object trajectories are preserved even at 1% pixel sampling.
- Reconstructed videos with fewer pixels still allow accurate object tracking.
- Trajectory graphs show minimal deviation from original videos.

## Abstract

Reducing the number of pixels in video signals while maintaining quality needed for recovering the trace of an object using Compressive Sensing is main subject of this work. Quality of frames, from video that contains moving object, are gradually reduced by keeping different number of pixels in each iteration, going from 45% all the way to 1%. Using algorithm for tracing object, results were satisfactory and showed mere changes in trajectory graphs, obtained from original and reconstructed videos.