This work describes a robust background subtraction scheme involving shadow and highlight removal for indoor environmental surveillance. Foreground regions can be precisely extracted by the proposed scheme despite illumination variations and dynamic background. The Gaussian mixture model (GMM) is applied to construct a color-based probabilistic background model (CBM). Based on CBM, the short-term color-based background model (STCBM) and the long-term color-based background model (LTCBM) can be extracted and applied to build the gradient-based version of the probabilistic background model (GBM). Furthermore, a new dynamic cone-shape boundary in the RGB color space, called a cone-shape illumination model (CSIM), is proposed to distinguish pixels among shadow, highlight, and foreground. A novel scheme combining the CBM, GBM, and CSIM is proposed to determine the background which can be used to detect abnormal conditions. The effectiveness of the proposed method is demonstrated via experiments with several video clips collected in a complex indoor environment.
The mean shift algorithm is a popular method in the field of 2D object tracking due to its simplicity and robustness over slight variations of lighting condition, scale and view-point over time. However, the appearance of 3D object might have distinctive variations for different viewpoints over time. In this work, a novel method for tracking 3D objects using mean-shift algorithm and a 3D object database is proposed to achieve a more precise tracking.A 3D object database using similarity-based aspect-graph is built from 2D images sampled at random intervals from the viewing sphere. Contour and color features of each 2D image are used for modeling the 3D object database. To conduct tracking, a suitable object model is selected from the database and the mean-shift tracking is applied to find the local minima of a similarity measure between the color histograms of the object model and the target image. The effectiveness of the proposed method is demonstrated by experiments with objects rotating and translating in space.
This work describes a new 3D cone-shape illumination model (CSIM) and a robust background subtraction scheme involving shadow and highlight removal for indoorenvironmental surveillance. Foreground objects can be precisely extracted for various post-processing procedures such as recognition. Gaussian mixture model (GMM) is applied to construct a color-based probabilistic background model (CBM) that contains the short-term color-based background model (STCBM) and the long-term color-based background model (LTCBM). STCBM and LTCBM are then proposed to build the gradient-based version of the probabilistic background model (GBM) and the CSIM. In the CSIM, a new dynamic cone-shape boundary in the RGB color space is proposed to distinguish pixels among shadow, highlight and foreground. Furthermore, CBM can be used to determine the threshold values of CSIM. A novel scheme combining the CBM, GBM and CSIM is proposed to determine the background. The effectiveness of the proposed method is demonstrated via experiments in a complex indoor environment.
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