# A fast multi-object tracking system using an object detector ensemble

## TL;DR

This paper introduces a multi-object tracking system that uses an ensemble of object detectors running periodically to improve real-time performance without significantly sacrificing accuracy.

## Contribution

It proposes a novel ensemble-based detection approach that enhances speed in multi-object tracking systems for real-time applications.

## Key findings

- Outperforms other online methods in speed on MOT16 benchmark.
- Maintains acceptable accuracy despite increased speed.
- Demonstrates effectiveness of detector ensembles in real-time MOT.

## Abstract

Multiple-Object Tracking (MOT) is of crucial importance for applications such as retail video analytics and video surveillance. Object detectors are often the computational bottleneck of modern MOT systems, limiting their use for real-time applications. In this paper, we address this issue by leveraging on an ensemble of detectors, each running every f frames. We measured the performance of our system in the MOT16 benchmark. The proposed model surpassed other online entries of the MOT16 challenge in speed, while maintaining an acceptable accuracy.