# OmniTrack: Real-time detection and tracking of objects, text and logos in video

arXiv: https://arxiv.org/abs/1910.06017 · HTML: https://mlsanity.com/paper/1910.06017/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Hyper360 -- a Next Generation Toolset for Immersive Media](https://mlsanity.com/paper/2108.00430/) (2021)
- [A real-time algorithm for human action recognition in RGB and thermal video](https://mlsanity.com/paper/2304.01567/) (2023)

## Abstract (arXiv)

The automatic detection and tracking of general objects (like persons, animals or cars), text and logos in a video is crucial for many video understanding tasks, and usually real-time processing as required. We propose OmniTrack, an efficient and robust algorithm which is able to automatically detect and track objects, text as well as brand logos in real-time. It combines a powerful deep learning based object detector (YoloV3) with high-quality optical flow methods. Based on the reference YoloV3 C++ implementation, we did some important performance optimizations which will be described. The major steps in the training procedure for the combined detector for text and logo will be presented. We will describe then the OmniTrack algorithm, consisting of the phases preprocessing, feature calculation, prediction, matching and update. Several performance optimizations have been implemented there as well, like doing the object detection and optical flow calculation asynchronously. Experiments show that the proposed algorithm runs in real-time for standard definition (720x576) video on a PC with a Quadro RTX 5000 GPU.