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Themen für studentische Arbeiten

Sachgebiet: Computer Vision

Distracted Driver Dataset

Thema Distracted Driver Dataset
Typ Master
Betreuer Okan Köpüklü, M.Sc.
Tel.: +49 (0)89 289-28554
E-Mail: okan.kopuklu@tum.de
Sachgebiet Computer Vision
Beschreibung Motivation: According to the last National Highway Traffic Safety Administration (NHTSA) report, one in ten fatal crashes and two in ten injury crashes were reported as distracted driver crashes in the United State in 2014. Therefore detecting the drivers distraction state is utmost important to reduce driver-related accidents. For this task, properly annotated dataset for drivers actions observation is necessary. With such a dataset, state-of-the art Deep Learning Architectures can be used to recognize the distraction state of the drivers.

Task: The main task is to collect a “Distracted Driver Dataset”, and use a light-weight Convolutional Neural Networks (CNN) architecture in order to detect driver’s distractive actions. The dataset should contain the following annotations:
1. Predefined distractive actions that the drivers do
2. Drivers hand states (whether they are on the wheel or not)

During the thesis, the following steps will be followed in general:
1. State-of-the-art research
2. Dataset collection and preparation (i.e. labeling and formating)
3. Light-weight CNN Architecture design
4. Evaluation of the CNN Architecture on the prepared dataset
5. Demonstration of the working system

References:
[1] Baheti, B., Gajre, S., & Talbar, S. (2018). Detection of Distracted Driver using Convolutional Neural Network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 1032-1038).
[2] Hssayeni, M. D., Saxena, S., Ptucha, R., & Savakis, A. (2017). Distracted driver detection: Deep learning vs handcrafted features. Electronic Imaging, 2017(10), 20-26.
[3] G. Borghi, E. Frigieri, R. Vezzani and R. Cucchiara, "Hands on the wheel: A Dataset for Driver Hand Detection and Tracking," 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi'an, 2018, pp. 564-570.
Voraussetzung 1. Excellent coding skills, preferable in Python
2. Experience in deep learning frameworks, preferably in Torch/PyTorch
3. Motivation to work on deep learning.
Bewerbung If you are interested in this topic, we welcome the applications via the email address above. Please set the email subject to " application for topic 'XYZ'", ex. "Master’s thesis application for topic 'XYZ'", while clearly specifying why are you interested in the topic in the text of the message. Also make sure to attach your most recent CV (if you have one) and grade report.

Real-time Detection and classification of Dynamic Hand Gestures

Thema Real-time Detection and classification of Dynamic Hand Gestures
Typ Forschungspraxis, Masterarbeit
Betreuer Okan Köpüklü, M.Sc.
Tel.: +49 (0)89 289-28554
E-Mail: okan.kupuklu@tum.de
Sachgebiet Computer Vision
Beschreibung Motivation : Detection and classification of dynamic hand gestures is a challenging task since there is no indication when an action starts in a video stream. However, most of the deep learning architectures which are working offline can also function online with proper adjustments. The topic of this thesis is convert an offline-working architecture to an online-working one.
Task : The main task is to bring an already working deep architecture, which can be seen below, to online functionality. Details of the architecture can be found in [1].
As a further reading, [2] also provides a detailed online detection architecture.


References :
[1] O. Köpüklü, N. Köse, and G. Rigoll. Motion fused frames: Data level fusion strategy for hand gesture recognition. arXiv preprint, arXiv:1804.07187, 2018.
[2] P. Molchanov, X. Yang, S. Gupta, K. Kim, S. Tyree, and J. Kautz. Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4207–4215, 2016.
Voraussetzung 1. Excellent coding skills in Python,
2. Experience in deep learning frameworks, preferably in Torch/PyTorch.
3. Motivation to work on deep learning.
Bewerbung If you are interested in this topic, we welcome the applications via the email address above. Please set the email subject to “ application for topic 'XYZ'”, ex. “Master’s thesis application for topic 'XYZ'”, while clearly specifying why are you interested in the topic in the text of the message. Also make sure to attach your most recent CV (if you have one) and grade report.