# An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models

## TL;DR

This paper explores parallelized motion masking using dual-mode single Gaussian models, implementing the method with TBB and CUDA, and analyzing speedup improvements for motion detection in video.

## Contribution

It introduces a parallel implementation of a dual-mode Gaussian motion masking technique using TBB and CUDA, with a comparative analysis of performance improvements.

## Key findings

- Parallel implementations significantly improve processing speed.
- CUDA outperforms TBB in speedup metrics.
- Theoretical analysis aligns with experimental results.

## Abstract

Motion detection in video is important for a number of applications and fields. In video surveillance, motion detection is an essential accompaniment to activity recognition for early warning systems. Robotics also has much to gain from motion detection and segmentation, particularly in high speed motion tracking for tactile systems. There are a myriad of techniques for detecting and masking motion in an image. Successful systems have used Gaussian Models to discern background from foreground in an image (motion from static imagery). However, particularly in the case of a moving camera or frame of reference, it is necessary to compensate for the motion of the camera when attempting to discern objects moving in the foreground. For example, it is possible to estimate motion of the camera through optical flow methods or temporal differencing and then compensate for this motion in a background subtraction model. We selection a method by Yi et al. using Dual-Mode Single Gaussian Models which does just this. We implement the technique in Intel's Thread Building Blocks (TBB) and NVIDIA's CUDA libraries. We then compare parallelization improvements with a theoretical analysis of speedups based on the characteristics of our selected model and attributes of both TBB and CUDA. We make our implementation available to the public.