Qnap Systems, Inc.

National Yang Ming Chiao Tung University

Altek Corporation

Horng-Horng Lin

Hong-Long Chou

Jen-Hui Chuang

Chia-Chun Tseng

Kuo-Hua Lo

An efficient 3D object localization method using multiple cameras is provided. The proposed method comprises a three-dimensional object localization process that firstly generates a plurality of two-dimensional line samples originated from a pre-calibrated vanishing point in each camera view for representing foreground video objects, secondly constructs a plurality of three-dimensional line samples from the two-dimensional line samples in all the multiple camera views, and thirdly determines three-dimensional object locations by clustering the three-dimensional line samples into object groups.

Video object localization method using multiple cameras

A background model learning system for lighting change adaptation for video surveillance is provided. The system includes a background model estimation unit that estimates a background model for a scene of interest; a foreground map construction unit that constructs a reference foreground map of the time instance; and a lighting change processing unit that revises the reference foreground map by reducing false foreground regions resulting from lighting changes. The revised foreground map is then sent back to both the background model estimation unit and the lighting change processing unit as feedbacks for model learning rate tuning in background model estimation and map integration in lighting change processing, respectively, for the next time instance.

Background model learning system for lighting change adaptation utilized for video surveillance

In the present invention, we identify that such a tradeoff between robustness to background changes and sensitivity to foreground abnormalities can be easily controlled by a new computational scheme of two-type learning rate control for the Gaussian mixture modeling (GMM). Based on the proposed rate control scheme, a new video surveillance system that applies feedbacks of pixel properties computed in object-level analysis to the learning rate controls of the GMM in pixel-level background modeling is developed. Such a system gives better regularization of background adaptation and is efficient in resolving the tradeoff for many surveillance applications.

Video surveillance system based on Gaussian mixture modeling with two-type learning rate control scheme

A method for adjusting photographing settings of a digital camera through motion detection, for determine to adjust the photographing settings of the digital camera according to vibrations of the digital camera or shaking of a photographed object. The adjusting method includes the following steps. At least two continuous pre-photographed images are captured. A central area and an edge area are set at same positions in the pre-photographed images respectively. Feature points are found in the pre-photographed images. A local motion vector and a camera motion vector are respectively determined according to a relation of the feature points on the central area and the edge area. A subject motion vector is generated according to a difference between the local motion vector and the camera motion vector. Adjustment of the photographing settings of the digital camera is determined according to a maximum value in the subject motion vector and camera motion vector.

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Hsinchu County, Taiwan

Horng-Horng Lin