PARTICLE FILTER WITH BINARY GAUSSIAN WEIGHTING AND SUPPORT VECTOR MACHINE FOR HUMAN POSE INTERPRETATION

This paper proposes human pose interpretation using particle filter (PF) with Binary Gaussian Weighting and support vector machine (SVM). In the proposed system, particle filter is used to track a human object, then this human object is skeletonized using thinning algorithm and classified using SVM. The classification is to identify human pose, whether it is normal or abnormal behavior. Here particle filter is modified through weight calculation using Gaussian distribution to reduce the computational time. The modified particle filter consists of four main phases. First, particles are generated to predict target’s location. Second, the weight of certain particles is calculated and these particles are used to build Gaussian distribution. Third, the weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The modified particle filter could reduce computational time of object tracking since this method does not have to calculate particle’s weight one by one. To calculate weight, the proposed method builds Gaussian distribution and calculates particle’s weight using this distribution. Through an experiment using video data taken in front of the cashier of a convenience store, the proposed method reduced computational time in tracking process until 68.34% in average compared to the conventional one, meanwhile the accuracy of tracking with this new method is comparable with particle filter method, i.e. 90.3%. Combining particle filter with binary Gaussian weighting and support vector machine is promising for advanced early crime scene investigation.