Affiliations: School of Electrical Engineering, University of Ulsan, Ulsan, Korea
Correspondence:
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Corresponding author. Kang-Hyun Jo, School of Electrical Engineering, University of Ulsan, 93 Deahak-road, Nam-gu, Ulsan, Korea. Tel.: +82 52 259 2208; Fax: +82 52 259 1686; E-mail: [email protected].
Note: [1] This work was supported by the Research Fund of University of Ulsan.
Abstract: Detecting human carrying baggage from video sequences is one of the important modules in identifying unattended baggage for video surveillance system. Hence, this paper addresses a framework for implementing such module. As the video was recorded using a static camera, the background modeling is firstly constructed for extracting foreground regions. These regions are considered as candidate of human by further verifying them using a general human detector. To identify whether the human is carrying baggage or not, the human region is divided into several components such as head, body, leg and baggage components according to the spatial information of baggage relative to a human body proportion. The scalable histogram of oriented gradient features of each component are extracted and the feature dimension is reduced by applying genetic algorithm. The features are trained using a support vector machine (SVM) over each component regarded as a weak classifier. The boosting machine is employed to combine these weak classifiers into a strong classifier for final decision. In experiment, standard public dataset are used to evaluate the effectiveness of our proposed approach. The results verified that the proposed framework outperforms the state-of-the-art methods and can be considered as one of the solutions for aforementioned task.
Keywords: Human carrying baggage detection, spatial component model, video surveillance, background modeling, boosting machine, genetic algorithm, support vector machine, scalable histogram of oriented gradient, unattended baggage
