Vehicle Activity Recognition using Mapped QTC Trajectories

Alaa AlZoubi, David Nam

2019

Abstract

The automated analysis of interacting objects or vehicles has many uses, including autonomous driving and security surveillance. In this paper we present a novel method for vehicle activity recognition using Deep Convolutional Neural Network (DCNN). We use Qualitative Trajectory Calculus (QTC) to represent the relative motion between pair of vehicles, and encode their interactions as a trajectory of QTC states. We then use one-hot vectors to map the trajectory into 2D matrix which conserves the essential position information of each QTC state in the sequence. Specifically, we project QTC sequences into a two dimensional image texture, and subsequently our method adapt layers trained on the ImageNet dataset and transfer this knowledge to the activity recognition task. We have evaluated our method using two different datasets, and shown that it out-performs state-of-the-art methods, achieving an error rate of no more than 1.16%. Our motivation originates from an interest in automated analysis of vehicle movement for the collision avoidance application, and we present a dataset of vehicle-obstacle interaction, collected from simulator-based experiments.

Paper Citation

in Harvard Style

AlZoubi A. and Nam D. (2019). Vehicle Activity Recognition using Mapped QTC Trajectories. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 27-38. DOI: 10.5220/0007307600270038

in Bibtex Style

@conference{visapp19,
author={Alaa AlZoubi and David Nam},
title={Vehicle Activity Recognition using Mapped QTC Trajectories},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={27-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007307600270038},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Vehicle Activity Recognition using Mapped QTC Trajectories
SN - 978-989-758-354-4
AU - AlZoubi A.
AU - Nam D.
PY - 2019
SP - 27
EP - 38
DO - 10.5220/0007307600270038
PB - SciTePress