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Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution

  • In this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture. To resolve occlusions of cyclists by other traffic participants or road structures, we use a wide angle stereo camera system mounted at a heavily frequented public intersection. We created a large dataset consisting of 1,639 video sequences containing cyclists, recorded in real world traffic, resulting in over 1.1 million samples. Through modeling the cyclists' behavior by a state machine of basic cyclist movements, our approach takes every situation into account and is not limited to certain scenarios. We compare our method to an approach solely based on position sequences. Both methods are evaluated taking into account frame wise and scene wise classification results of basic movements, and detection times of basic movement transitions, where our approach outperforms the position based approach by producing more reliable detections with shorter detection times. Our code and parts of our dataset are made publicly available.

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Metadaten
Author:Stefan Zernetsch, Steven Schreck, Viktor Kress, Konrad Doll, Bernhard Sick
URL:https://ieeexplore.ieee.org/document/9413233
Parent Title (English):2020 25th International Conference on Pattern Recognition (ICPR)
Document Type:Conference Proceeding
Language:English
Year of Completion:2020
Release Date:2021/09/16
GND Keyword:Fahrerassistenzsystem; Radfahrer
Urheberrecht:1
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Intelligent Systems / Intelligent Mobility
Intelligent Systems / Intelligent Sensors and Signals
Licence (German):Keine Lizenz - es gilt das deutsche Urheberrecht
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