# Development of a N-type GM-PHD Filter for Multiple Target, Multiple Type Visual Tracking

## TL;DR

This paper introduces an N-type GM-PHD filter based on Random Finite Set theory for multi-target, multi-type visual tracking, addressing detection confusions and clutter, and demonstrates improved performance on real videos.

## Contribution

It extends the Gaussian mixture GM-PHD filter to handle multiple target types and detection confusions, a novel advancement in multi-target tracking.

## Key findings

- Improved tracking accuracy over raw detection methods.
- Effective handling of detection confusions among target types.
- Enhanced performance demonstrated on real video sequences.

## Abstract

We propose a new framework that extends the standard Probability Hypothesis Density (PHD) filter for multiple targets having $N\geq2$ different types based on Random Finite Set theory, taking into account not only background clutter, but also confusions among detections of different target types, which are in general different in character from background clutter. Under Gaussianity and linearity assumptions, our framework extends the existing Gaussian mixture (GM) implementation of the standard PHD filter to create a N-type GM-PHD filter. The methodology is applied to real video sequences by integrating object detectors' information into this filter for two scenarios. For both cases, Munkres's variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames. This approach is evaluated and compared to both raw detection and independent GM-PHD filters using the Optimal Sub-pattern Assignment metric and discrimination rate. This shows the improved performance of our strategy on real video sequences.