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You searched for +publisher:"Delft University of Technology" +contributor:("Domhof, Joris"). Showing records 1 – 5 of 5 total matches.

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Delft University of Technology

1. Immerzeel, Ronald (author). Robust Tracking Approach for Dealing with Classification Uncertainty.

Degree: 2018, Delft University of Technology

Every year about 1.25 million people die as a result of road traffic accidents. Besides the traffic on the road increases every day, including the… (more)

Subjects/Keywords: Object tracking; Classification; RCA-CPHD; classification uncertainty; Multiple Model CPHD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Immerzeel, R. (. (2018). Robust Tracking Approach for Dealing with Classification Uncertainty. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:abe30b3f-b2f4-4a4e-9b34-fc42159a0e0e

Chicago Manual of Style (16th Edition):

Immerzeel, Ronald (author). “Robust Tracking Approach for Dealing with Classification Uncertainty.” 2018. Masters Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:abe30b3f-b2f4-4a4e-9b34-fc42159a0e0e.

MLA Handbook (7th Edition):

Immerzeel, Ronald (author). “Robust Tracking Approach for Dealing with Classification Uncertainty.” 2018. Web. 28 Jan 2021.

Vancouver:

Immerzeel R(. Robust Tracking Approach for Dealing with Classification Uncertainty. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:abe30b3f-b2f4-4a4e-9b34-fc42159a0e0e.

Council of Science Editors:

Immerzeel R(. Robust Tracking Approach for Dealing with Classification Uncertainty. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:abe30b3f-b2f4-4a4e-9b34-fc42159a0e0e


Delft University of Technology

2. Wymenga, Jan (author). Weather Condition Estimation in Automated Vehicles.

Degree: 2018, Delft University of Technology

This work presents a multi-sensor approach for weather condition estimation in automated vehicles. Using combined data from weather sensors (barometer, hygrometer, etc) and an in-vehicle… (more)

Subjects/Keywords: weather types; machine learning; intelligent vehicles; vision; driving; weather

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wymenga, J. (. (2018). Weather Condition Estimation in Automated Vehicles. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94

Chicago Manual of Style (16th Edition):

Wymenga, Jan (author). “Weather Condition Estimation in Automated Vehicles.” 2018. Masters Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94.

MLA Handbook (7th Edition):

Wymenga, Jan (author). “Weather Condition Estimation in Automated Vehicles.” 2018. Web. 28 Jan 2021.

Vancouver:

Wymenga J(. Weather Condition Estimation in Automated Vehicles. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94.

Council of Science Editors:

Wymenga J(. Weather Condition Estimation in Automated Vehicles. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94


Delft University of Technology

3. Katsaounis, Georgios (author). Extended Object Tracking of Pedestrians in Automotive Applications.

Degree: 2019, Delft University of Technology

 Recent advances in sensor technology have lead to increased resolution of novel sensors, while tracking applications where distance between sensors and objects of interest is… (more)

Subjects/Keywords: Extended Object Tracking; Vulnurable Road Users; Pedestrians; Environmental Perception; Automotive Applications; Lidar sensor; Mono camera sensor; Sensor Fusion; Random Matrix Model; Elliptical shape; OpenPose library; Human Pose Detections; position measurement; heading angle measurement; Extended Kalman Filter; Kalman Filter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Katsaounis, G. (. (2019). Extended Object Tracking of Pedestrians in Automotive Applications. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749

Chicago Manual of Style (16th Edition):

Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Masters Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749.

MLA Handbook (7th Edition):

Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Web. 28 Jan 2021.

Vancouver:

Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749.

Council of Science Editors:

Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749


Delft University of Technology

4. Sahla, Nordin (author). A Deep Learning Prediction Model for Object Classification.

Degree: 2018, Delft University of Technology

 The last decade has marked a rapid and significant growth of the global market of warehouse automation. The biggest challenge lies in the identification and… (more)

Subjects/Keywords: Machine Learning; Convolutional Neural Network; object recognition; Barcode localization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sahla, N. (. (2018). A Deep Learning Prediction Model for Object Classification. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f7667cb4-70d4-4b82-ac1b-75df476655cd

Chicago Manual of Style (16th Edition):

Sahla, Nordin (author). “A Deep Learning Prediction Model for Object Classification.” 2018. Masters Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:f7667cb4-70d4-4b82-ac1b-75df476655cd.

MLA Handbook (7th Edition):

Sahla, Nordin (author). “A Deep Learning Prediction Model for Object Classification.” 2018. Web. 28 Jan 2021.

Vancouver:

Sahla N(. A Deep Learning Prediction Model for Object Classification. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:f7667cb4-70d4-4b82-ac1b-75df476655cd.

Council of Science Editors:

Sahla N(. A Deep Learning Prediction Model for Object Classification. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:f7667cb4-70d4-4b82-ac1b-75df476655cd


Delft University of Technology

5. GAO, Xinyu (author). Sensor Data Fusion of Lidar and Camera for Road User Detection.

Degree: 2018, Delft University of Technology

Object detection is one of the most important research topics in autonomous vehicles. The detection systems of autonomous vehicles nowadays are mostly image-based ones which… (more)

Subjects/Keywords: 3D object detection; Lidar; Camera; sensor fusion

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

GAO, X. (. (2018). Sensor Data Fusion of Lidar and Camera for Road User Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a

Chicago Manual of Style (16th Edition):

GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.

MLA Handbook (7th Edition):

GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Web. 28 Jan 2021.

Vancouver:

GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.

Council of Science Editors:

GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a

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