Authors: Maarten Bieshaar,Malte Depping,Jan Schneegans,Bernhard Sick
ArXiv: 1808.04449
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Abstract URL: http://arxiv.org/abs/1808.04449v1
In near future, vulnerable road users (VRUs) such as cyclists and pedestrians
will be equipped with smart devices and wearables which are capable to
communicate with intelligent vehicles and other traffic participants. Road
users are then able to cooperate on different levels, such as in cooperative
intention detection for advanced VRU protection. Smart devices can be used to
detect intentions, e.g., an occluded cyclist intending to cross the road, to
warn vehicles of VRUs, and prevent potential collisions. This article presents
a human activity recognition approach to detect the starting movement of
cyclists wearing smart devices. We propose a novel two-stage feature selection
procedure using a score specialized for robust starting detection reducing the
false positive detections and leading to understandable and interpretable
features. The detection is modelled as a classification problem and realized by
means of a machine learning classifier. We introduce an auxiliary class, that
models starting movements and allows to integrate early movement indicators,
i.e., body part movements indicating future behaviour. In this way we improve
the robustness and reduce the detection time of the classifier. Our empirical
studies with real-world data originating from experiments which involve 49 test
subjects and consists of 84 starting motions show that we are able to detect
the starting movements early. Our approach reaches an F1-score of 67 % within
0.33 s after the first movement of the bicycle wheel. Investigations concerning
the device wearing location show that for devices worn in the trouser pocket
the detector has less false detections and detects starting movements faster on
average. We found that we can further improve the results when we train
distinct classifiers for different wearing locations.