| 1. |
- Ahlberg, Erik, et al.
(författare)
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"Vi klimatforskare stödjer Greta och skolungdomarna"
- 2019
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Ingår i: Dagens nyheter (DN debatt). - 1101-2447.
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- DN DEBATT 15/3. Sedan industrialiseringens början har vi använt omkring fyra femtedelar av den mängd fossilt kol som får förbrännas för att vi ska klara Parisavtalet. Vi har bara en femtedel kvar och det är bråttom att kraftigt reducera utsläppen. Det har Greta Thunberg och de strejkande ungdomarna förstått. Därför stödjer vi deras krav, skriver 270 klimatforskare.
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| 2. |
- Ardö, Håkan, et al.
(författare)
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Reduced Search Space for Rapid Bicycle Detection
- 2013
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Ingår i: [Host publication title missing]. - : SciTePress - Science and and Technology Publications.
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Konferensbidrag (refereegranskat)abstract
- This paper describes a solution to the application of rapid detection of bicycles in low resolution video. In particular, the application addressed is from video recorded in a live environment. The future aim from the results in this paper is to investigate a full year of video data. Hence, processing speed is of great concern. The proposed solution involves the use of an object detector and a search space reduction method based on prior knowledge regarding the application at hand. The method using prior knowledge utilizes random sample consensus, and additional statistical analysis on detection outputs, in order to define a reduced search space. It is experimentally shown that, in the application addressed, it is possible to reduce the full search space by 62% with the proposed methodology. This approach, which employs a full detector in combination with the design of a simple and fast model that can capture prior knowledge for a specific application, leads to a reduced search space and thereby a significantly improved processing speed.
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| 3. |
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| 4. |
- Laureshyn, Aliaksei, et al.
(författare)
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Automated video analysis as a tool for analysing road user behaviour
- 2006
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Ingår i: Proceedings of ITS World Congress, London, 8-12 October 2006.
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Konferensbidrag (refereegranskat)abstract
- At Lund University an automated video analysis system is being developed that can be applied for studying the behaviour of road users in complex traffic environments. It is stressed that system must be capable of handling all the categories of road users, i.e. vehicles, pedestrians and cyclists. Common problems like detection and tracking of moving objects, occlusion by foreground objects, ground-plane co-ordinates estimation, smoothing of the scattered data and estimation of speed and acceleration profiles are discussed and some solution proposed.
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| 5. |
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| 6. |
- 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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| 7. |
- 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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| 8. |
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| 9. |
- Ardö, Håkan, et al.
(författare)
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Superpixel based road user tracker
- 2014
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Abstract A superpixel based tracker is tested on the two Tracking sequences from PDTV [7], Minsk and Sherbrooke. It detects all vehicles from the Minsk dataset although a few of them are splitted. The pedestrians are too small and thus all missed. The results for the Sherbrooke are not as good, especially in the areas far way from the camera where the intersection s viewed at a low angle. Also the sign in the foreground causes misses.
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| 10. |
- Nilsson, Mikael, et al.
(författare)
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A search space strategy for pedestrian detection and localization in world coordinates
- 2018
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Ingår i: VISAPP. - 9789897582905 ; 5, s. 17-24
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Konferensbidrag (refereegranskat)abstract
- The focus of this work is detecting pedestrians, captured in a surveillance setting, and locating them in world coordinates. Commonly adopted search strategies operate in the image plane to address the object detection problem with machine learning, for example using scale-space pyramid with the sliding windows methodology or object proposals. In contrast, here a new search space is presented, which exploits camera calibration information and geometric priors. The proposed search strategy will facilitate detectors to directly estimate pedestrian presence in world coordinates of interest. Results are demonstrated on real world outdoor collected data along a path in dim light conditions, with the goal to locate pedestrians in world coordinates. The proposed search strategy indicate a mean error under 20 cm, while image plane search methods, with additional processing adopted for localization, yielded around or above 30 cm in mean localization error. This while only observing 3-4% of patches required by the image plane searches at the same task.
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