Lidé na fakultě
RNDr. Júlia Škovierová, Ph.D.

2019, 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP), Piscataway, NJ, IEEE), p. 19-25), ISBN 978-1-7281-4914-1

In autonomous driving systems, the intention estimation of traffic participants is one of the most crucial aspects. In this paper, several machine learning methods are used to train classifiers capable of estimating the intention of a pedestrian to cross a zebra crossing. Their results are compared to a Bayesian network – an approach commonly used in autonomous driving. The data used for the estimation contain only position and heading of the pedestrians. The best performing method achieved the F2 score of 92.37%. Index Terms—Intention estimation, Machine learning, Automated driving.

Stať ve sborníku z prestižní konf.

2018, Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, Porto, SciTePress - Science and Technology Publications), p. 341-348), ISBN 978-989-758-293-6

The reported work contributes to the self-driving car efforts, more specifically to scenario understanding from the ego-car point of view. We focus on estimating the intentions of pedestrians near a zebra crossing. First, we predict the future motion of detected pedestrians in a three seconds time horizon. Second, we estimate the intention of each pedestrian to cross the street using a Bayesian network. Results indicate, that the dependence between the error rate of motion prediction and the intention estimation is sub-linear. Thus, despite the lower performance of motion prediction for the time scope larger than one second, the intention estimation remains relatively stable.

Stať ve sborníku z prestižní konf. (Scopus)

2018, Neurocomputing, 307, p. 172-183), ISSN 0925-2312

The essential goal of this paper is to extend the functionality of the bio-inspired intelligent HTM (Hierarchical Temporal Memory) network towards two capabilities: (i) object recognition in color images, (ii) detection of multiple objects located in clutter color images. The former extension is based on the development of a novel scheme for the application of three parallel HTM networks that separately process color, texture, and shape information in color images. For the latter HTM extension, we propose a novel system in which HTM is combined with a modified model of computational visual attention. We adopt the results of Bi et al. (2010), Hu et al. (2005), and Kučerová (2011) and add new elements for the calculation of image saliency maps. The proposed algorithm enables one to automatically locate individual objects in clutter images. For computer experiments, a special image database is created to simulate ideal single object images and cluttered images with multiple objects on an inhomogeneous background. The recognition performance of HTM alone and in combination with the salient-region detection method is evaluated. We show that the attention subsystem is able to satisfactorily locate multiple objects in clutter color images with an inhomogeneous background. We also perform benchmark calculations for two selected computer vision methods used for object detection in color clutter images. Namely, the cascade detector and template matching methods are used. Our study confirms that the proposed attention system can improve the capabilities of HTM for object classification in cluttered images. The compound system of visual attention and HTM outperforms the compared methods in both criteria (recall and correct detection rate). However, as expected, the system cannot match the recognition accuracy achieved by HTM for single object images, and thus, further research is needed.

Článek v periodiku excerpovaném SCI Expanded

2018, ISBN 978-80-270-3395-9

Sborník z mezinár. konf. cizojazyčně