This paper presents a classification approach for road‐user modes of travel. The classification does not assume well organized, and lane disciplined traffic. Instead, it relies on specific characteristics intrinsic for each road‐user to predict the corresponding class. The classification relies on extracting the geometric and movement characteristics of road‐users. As such, it is possible to classify road‐users in shared space facilities and sites with high level of non‐compliance. The classification is a multi‐step procedure. First, movement features are used to discriminate between motorized and non‐motorized road‐users. Then, complementary features based on road‐user geometry are added to differentiate between vehicles, rickshaws, powered two‐wheelers, and buses. Experiments are performed on a video data set from a shared facility in New Delhi, India. A performance analysis demonstrated the robustness of the proposed classification method with a correct classification rate of up to 90 percent. By considering the movement attributes, the approach is tolerant to considerable variations in road‐user physical details which often arises from choices of camera positions and partial occlusions. The research is part of the long‐term goal to develop an automated video‐based road safety and data collection system for developing countries.
Automated class identification of modes of travel in shared spaces: a case study from India
IET Intelligent Transport Systems ; 12 , 8 ; 765-773
2018-10-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
India , lane disciplined traffic , geometric extraction , road‐user movement characteristics , multistep classification procedure , New Delhi , camera positions , partial occlusions , road safety , road‐user geometry , road‐user modes , sensitivity analysis , video cameras , complementary features , traffic engineering computing , feature extraction , shared space facilities , video signal processing , automated video‐based road safety , image classification , automated class identification , data collection system , video dataset
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