AlZoubi, Alaa and Nam, David (2020) Vehicle Activity Recognition Using DCNN. In: Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2019. Communications in Computer and Information Science (1182). Springer, Cham, pp. 566-588. ISBN 978-3-030-41589-1
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Abstract
Abstract. This paper presents a novel Deep Convolutional Neural Net
work (DCNN) method for vehicle activity classification. We extend our
previous approach to be able to classify a larger number of vehicle trajec
tories in a single network.We also highlight the fl
exibility of our approach
in integrating further scenarios to our classifier. Firstly, a spatiotempo
ral calculus method is used to encode the relative movement between
vehicles as a trajectory of QTC states. We then map the encoded trajectory to a 2D matrix using the one-hot vector mapping, this preserves
the important positional data and order for each QTC state. To do this
we associate the QTC sequences with pixels to form a 2D image tex
ture. Afterwards, we adapted trained CNN architecture into our vehicles
activity recognition task. Two separate types of driving data sets are
used to evaluate our method. We demonstrate that the proposed method
out-performs existing techniques. Along with the proposed approach we
created a new dataset of vehicles interactions. Although the focus of this
paper is on the automated analysis of vehicle interactions, the proposed
technique is general and can be applied for pairwise analysis for moving
objects.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | Vehicle Activity Classifcation; Spatiotemporal 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: | 11 Mar 2020 15:59 |
| Last Modified: | 20 Feb 2022 01:15 |
| URI: | https://bear.buckingham.ac.uk/id/eprint/459 |
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