Nonparametric state machine with multiple features for abnormal object classification
Jiman Kim, Bong-Nam Kang · 2014
Abandoned object and removed object are important abnormal objects in visual surveillance area to predict the crimes such as explosion or theft event. In real situations, most of existing methods using CCD camera show inconsistent performance because they use a lot of threshold values depending on the environmental conditions of target scene such as illumination change, high traffic volume and complex background. We propose a nonparametric state machine with hierarchical structure consisting of three layers. As shown in the experimental results, the proposed method can be applied to general situations because the state transitions is performed by trained SVM classifiers.