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- Laureshyn, Aliaksei, et al.
(författare)
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From Speed Profile Data to Analysis of Behaviour
- 2009
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Ingår i: IATSS Research. - 0386-1112. ; 33:2, s. 88-98
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Tidskriftsartikel (refereegranskat)abstract
- Classification of speed profiles is necessary to allow interpretation of automatic speed measurements in terms of road user behaviour. Aggregation without considering variation in individual profile shapes easily leads to aggregation bias, while classification based on exogenous criteria runs the risk of loosing important information on behavioural (co-) variation. In this paper we test how three pattern recognition techniques (cluster analysis, supervised learning and dimension reduction) can be applied to automatically classify the shapes of speed profiles of individual vehicles into interpretable types, with a minimum of a priori assumptions. The data for the tests is obtained from an automated video analysis system and the results of automated classification are compared to the classification by a human observer done from the video. Normalisation of the speed profiles to a constant number of data points with the same spatial reference allows them to be treated as multidimensional vectors. The k-means clustering algorithm groups the vectors (profiles) based on their proximity in multidimensional space. The results are satisfactory, but still the least successful among the tested techniques. Supervised learning (nearest neighbour algorithm tested) uses a training dataset produced beforehand to assign a profile to a specific group. Manual selection of the profiles for the training dataset allows better control of the output results and the classification results are the most successful in the tests. Dimension reduction techniques decrease the amount of data representing each profile by extracting the most typical “features”, which allows for better data visualisation and simplifies the classification procedures afterwards. The singular value decomposition (SVD) used in the test performs quite satisfactorily. The general conclusion is that pattern recognition techniques perform well in automated classification of speed profiles compared to classification by a human observer. However, there are no given rules on which technique will perform best.
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- Saunier, Nicolas, et al.
(författare)
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A Public Video Dataset for Road Transportation Applications
- 2014
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Konferensbidrag (refereegranskat)abstract
- Video data and the tools for automated analysis have a great potential to be used in road traffic research, particularly road safety. In this project a video dataset is built and made public so that researchers can evaluate their algorithms on it. The dataset focuses on the traffic research applications (data from real research projects) and provides recordings of the traffic scenes, meta-data, camera calibration, ground truth, protocols for comparing algorithms and software tools and libraries for reading/presenting the data. To the authors’ knowledge, this public dataset is the first of its kind. With the proposed dataset, researchers get access to a large variety of recordings representing different traffic, weather and lighting conditions to evaluate and compare different tools and applications. As a consequence, discussions between computer vision and transportation researchers are expected to increase, contributing to more collaborations and better tools, more accurate and user-friendly, to obtain automatically rich traffic data from video.
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