Satoshi Yoshinaga

We propose two types of spatiotemporal background modeling frameworks that can adapt to illumination and dynamic changes in the background. Expand

We propose a system for counting the number of pedestrians in real-time using adaptive background modeling using Parzen density estimation. Expand

We propose a new probabilistic background model integrating pixel-based and spatial-based models by considering the illumination fluctuation in localized regions. Expand

We present a robust background model for object detection and report its evaluation results using the database of Background Models Challenge (BMC). Expand

To robustly detect foreground objects against illumination changes, we propose a new background model considering the similarity in the intensity changes among pixels. Expand

We propose a new background model in this paper by applying a statistical framework to a local feature-based approach to handle motion changes in the background. Expand

A network environment that unifies the human movement, animation and humanoid is generated. Expand

In this paper, we propose 1) a method to inhibit background training, and 2)A method to update an original background region occluded by stopped object. Expand

We propose a framework for multi-layered background modeling, in which we conserve the background models for stationary objects hierarchically in addition to the one for the initial background, and propose a spatio-temporal background model based on the similarity in the intensity changes among pixels. Expand