We address the automatic recognition of road safety attributes according to the iRAP methodology. We formulate the problem as a separate multi-class classification of each iRAP attribute in georeferenced video clips that correspond to particular road segments. We propose a solution based on an efficient multi-task model with shared features, which can recognize all attributes with a single forward pass and learn in an end-to-end fashion. We perform experiments on a novel real dataset acquired along 1850 km of public roads in Bosnia and Herzegovina, in which all iRAP attributes have been annotated by human experts. We express recognition accuracy as per-attribute macro-F1 scores due to a significant class imbalance present within most attributes. We thoroughly validate different variants of our model, analyze the contributions of several hyper-parameters, and report recognition accuracy on the independent test set.


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    Title :

    Multi-Task Learning for iRAP Attribute Classification and Road Safety Assessment


    Contributors:


    Publication date :

    2020-09-20


    Size :

    968413 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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