75,392 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions
Preparation of high-quality datasets for the urban scene understanding is a
labor-intensive task, especially, for datasets designed for the autonomous
driving applications. The application of the coarse ground truth (GT)
annotations of these datasets without detriment to the accuracy of semantic
image segmentation (by the mean intersection over union - mIoU) could simplify
and speedup the dataset preparation and model fine tuning before its practical
application. Here the results of the comparative analysis for semantic
segmentation accuracy obtained by PSPNet deep learning architecture are
presented for fine and coarse annotated images from Cityscapes dataset. Two
scenarios were investigated: scenario 1 - the fine GT images for training and
prediction, and scenario 2 - the fine GT images for training and the coarse GT
images for prediction. The obtained results demonstrated that for the most
important classes the mean accuracy values of semantic image segmentation for
coarse GT annotations are higher than for the fine GT ones, and the standard
deviation values are vice versa. It means that for some applications some
unimportant classes can be excluded and the model can be tuned further for some
classes and specific regions on the coarse GT dataset without loss of the
accuracy even. Moreover, this opens the perspectives to use deep neural
networks for the preparation of such coarse GT datasets.Comment: 10 pages, 6 figures, 2 tables, The Second International Conference on
Computer Science, Engineering and Education Applications (ICCSEEA2019) 26-27
January 2019, Kiev, Ukrain
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