AlZoubi, Alaa and Nam, David (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. SciTePress Digital Library (Science and Technology Publications, Lda), Portugal, pp. 27-38. ISBN 978-989-758-354-4
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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.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | Vehicle Activity Recognition, Qualitative Trajectory Calculus, Trajectory Texture, Transfer Learning, Deep Convolutional Neural Networks |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | School of Computing |
| Depositing User: | Alaa AlZoubi |
| Date Deposited: | 31 Jul 2019 09:23 |
| Last Modified: | 31 Jul 2019 09:23 |
| URI: | https://bear.buckingham.ac.uk/id/eprint/389 |
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