# Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment

## TL;DR

This paper introduces an instance-aware multi-object tracker with dynamic model refreshment that improves robustness and accuracy in multi-object tracking by integrating SOT techniques and adaptive model updating.

## Contribution

It proposes a novel instance-aware tracking framework with a learned model refreshing strategy to enhance multi-object tracking performance.

## Key findings

- Achieves state-of-the-art results on MOT15 and MOT16 benchmarks.
- Effectively distinguishes targets from similar objects and background.
- Reduces initialization noise and adapts to target appearance changes.

## Abstract

Recent progresses in model-free single object tracking (SOT) algorithms have largely inspired applying SOT to \emph{multi-object tracking} (MOT) to improve the robustness as well as relieving dependency on external detector. However, SOT algorithms are generally designed for distinguishing a target from its environment, and hence meet problems when a target is spatially mixed with similar objects as observed frequently in MOT. To address this issue, in this paper we propose an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models. In particular, we construct each target model by fusing information for distinguishing target both from background and other instances (tracking targets). To conserve uniqueness of all target models, our instance-aware tracker considers response maps from all target models and assigns spatial locations exclusively to optimize the overall accuracy. Another contribution we make is a dynamic model refreshing strategy learned by a convolutional neural network. This strategy helps to eliminate initialization noise as well as to adapt to the variation of target size and appearance. To show the effectiveness of the proposed approach, it is evaluated on the popular MOT15 and MOT16 challenge benchmarks. On both benchmarks, our approach achieves the best overall performances in comparison with published results.