Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering
Paper in proceedings, 2018

Monocular cameras are one of the most commonly used sensors in the automotive industry for autonomous vehicles. One major drawback using a monocular camera is that it only makes observations in the two dimensional image plane and can not directly measure the distance to objects. In this paper, we aim at filling this gap by developing a multi-object tracking algorithm that takes an image as input and produces trajectories of detected objects in a world coordinate system. We solve this by using a deep neural network trained to detect and estimate the distance to objects from a single input image. The detections from a sequence of images are fed in to a state-of-the art Poisson multi-Bernoulli mixture tracking filter. The combination of the learned detector and the PMBM filter results in an algorithm that achieves 3D tracking using only mono-camera images as input. The performance of the algorithm is evaluated both in 3D world coordinates, and 2D image coordinates, using the publicly available KITTI object tracking dataset. The algorithm shows the ability to accurately track objects, correctly handle data associations, even when there is a big overlap of the objects in the image, and is one of the top performing algorithms on the KITTI object tracking benchmark. Furthermore, the algorithm is efficient, running on average close to 20 frames per second.

Author

Samuel Scheidegger

Zenuity

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Joachim Benjaminsson

Zenuity

Chalmers, Electrical Engineering

Emil Rosenberg

Chalmers, Electrical Engineering

Amrit Krishnan

Zenuity

Karl Granström

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

IEEE Intelligent Vehicles Symposium, Proceedings

Vol. 2018-June 433-440 8500454

2018 IEEE Intelligent Vehicles Symposium, IV 2018
Changshu, Suzhou, China,

COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA, 2016-01-01 -- 2018-12-31.

Areas of Advance

Transport

Subject Categories

Robotics

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

DOI

10.1109/IVS.2018.8500454

More information

Latest update

5/10/2019