@inproceedings{Ruehle14:FISITA,
    type = {inproceedings},
    key = {Ruehle14:FISITA},
    title = {Vulnerability Classification of Generic Object Hypotheses using a Visual Words Approach},
    author = {Johannes Rühle and Maxim Arbitmann and Joachim Denzler},
    booktitle = {FISITA World Automotive Congress (FISITA)},
    year = {2014},
    note = {F2014-AST-045},
    pages = {1-5},
    abstract = {We present a method based on image processing to evaluate the vulnerability of objects detected in front of a vehicle by means of a stereo camera. The evaluation is part of the current cooperate research project UR:BAN SVT, which is introduced and described in this paper. The project's main objective is to further increase road safety for vulnerable road users. The detection of potentially vulnerable real world objects is performed by a car build-in stereo camera that outputs object hypotheses as medium-level object representations. Given these generic object hypotheses, our method classifies an object's vulnerability that states the expected damage of a car collision from the object's perspective. This information about obstacle hypotheses enables a better static scene understanding and thereby can be used to plan actions for accident prevention and mitigation in emergency situations. Our approach focuses on employing a model-free classification pipeline using bags-of-visual words extracted in a completely unsupervised manner. The results show that the bag-of-visual-words approach is well-suited for evaluating the vulnerability of object hypotheses.},
    owner = {Ruehle},
}