# aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge

## TL;DR

This paper introduces a lightweight, real-time object detection and tracking system called aUToTrack, which integrates vision, LIDAR, and GPS/IMU data to accurately track pedestrians and vehicles for self-driving cars, achieving state-of-the-art results.

## Contribution

The paper presents a novel dataset (UofTPed50) for pedestrian tracking and a lightweight system (aUToTrack) that combines multiple sensors for accurate, real-time object tracking in autonomous driving.

## Key findings

- Achieves state-of-the-art performance on KITTI benchmark.
- Accurately estimates pedestrian positions and velocities in real-time.
- Operates efficiently on CPUs in real-world driving conditions.

## Abstract

The University of Toronto is one of eight teams competing in the SAE AutoDrive Challenge -- a competition to develop a self-driving car by 2020. After placing first at the Year 1 challenge, we are headed to MCity in June 2019 for the second challenge. There, we will interact with pedestrians, cyclists, and cars. For safe operation, it is critical to have an accurate estimate of the position of all objects surrounding the vehicle. The contributions of this work are twofold: First, we present a new object detection and tracking dataset (UofTPed50), which uses GPS to ground truth the position and velocity of a pedestrian. To our knowledge, a dataset of this type for pedestrians has not been shown in the literature before. Second, we present a lightweight object detection and tracking system (aUToTrack) that uses vision, LIDAR, and GPS/IMU positioning to achieve state-of-the-art performance on the KITTI Object Tracking benchmark. We show that aUToTrack accurately estimates the position and velocity of pedestrians, in real-time, using CPUs only. aUToTrack has been tested in closed-loop experiments on a real self-driving car, and we demonstrate its performance on our dataset.