23 May 2013 Stochastic process modeling for multiple human tracking using stereo video camera
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Abstract
Recently microscopic understanding of individual pedestrian behavior in public space is becoming significant. Observation data from diverse sensors have increased. Meanwhile some simulation models of human behavior have made progress. This paper proposes a method of multiple human tracking under the complex situations by integrating the various observation data and the simulation. The key concept is that the multiple human tracking can be regarded as stochastic process modeling. A data assimilation technique is employed as the stochastic process modeling. The data assimilation technique consists of observations, forecasting and filtering. For the modeling, a state vector is defined as an ellipsoid and its coordinates, which are human positions and shapes. An observation vector is also defined as observations from stereo video camera, namely color and range information. Then a system model which represents dynamics of the state vectors is formulated by using discrete choice model. The discrete choice model decides the next step of each pedestrian stochastically and deals with interaction between pedestrians. An observation model is also formulated for the filtering step. The likelihood of color is modeled based on color histogram matching, and one of range is calculated by comparing between the ellipsoidal model and observed 3D data. The proposed method is applied to the data acquired at the ticket gate of a station and the high performance of the method is confirmed. We compare the results with other models and show the advantage of integrating the behavior model to the tracking method.
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Takashi Fuse, Takashi Fuse, Wataru Nakanishi, Wataru Nakanishi, } "Stochastic process modeling for multiple human tracking using stereo video camera", Proc. SPIE 8791, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 87910L (23 May 2013); doi: 10.1117/12.2020425; https://doi.org/10.1117/12.2020425
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