Abstract
This paper introduces a new approach for detecting carried baggage by constructing human-baggage detector. It utilizes the spatial information of baggage in relevance to the human body carrying it. Human-baggage detector is modeled by body part of human, such as head, torso, leg and bag. The SVM is then used for training each part model. The boosting approach is constructing a strong classification by combining a set of weak classifier for each body part. Specify for bag part, the mixture model is built for overcoming strong variation of shape, color, and size. The proposed method has been extensively tested using public dataset. The experimental results suggest that the proposed method can be alternative method for state-of-the art baggage detection and classification algorithm.
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Wahyono, Jo, KH. (2015). Carried Baggage Detection and Classification Using Part-Based Model. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_31
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DOI: https://doi.org/10.1007/978-3-319-22053-6_31
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