# FANTrack: 3D Multi-Object Tracking with Feature Association Network

## TL;DR

FANTrack introduces a CNN-based data association method for online 3D multi-object tracking, effectively handling noisy detections and varying target numbers, achieving competitive results on KITTI.

## Contribution

The paper presents a novel deep learning approach that formulates data association as CNN inference, enabling global 3D tracking from data with improved robustness and simplicity.

## Key findings

- Achieves competitive performance on KITTI dataset
- Handles noisy detections effectively
- Supports varying number of targets

## Abstract

We propose a data-driven approach to online multi-object tracking (MOT) that uses a convolutional neural network (CNN) for data association in a tracking-by-detection framework. The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. Our solution learns to perform global assignments in 3D purely from data, handles noisy detections and a varying number of targets, and is easy to train. We evaluate our approach on the challenging KITTI dataset and show competitive results. Our code is available at https://git.uwaterloo.ca/wise-lab/fantrack.