Gorilla Technology Inc.

Yuang-Tzong Lan

Sze-Yao Ni

Yi-Wei Chen

Kuo-Chen Wu

Tom C I Lin

Tom Ci Lin

Jiann Cherng Luo

The present invention discloses a method of evaluating image quality taken in an actual scene, the method comprising: providing a test chart placed in an actual scene, said test chart comprising a plurality of positioning patterns and a plurality of quality test patterns; capturing an image of the test chart in the actual scene; searching the plurality of positioning patterns in the image of the test chart to locate the orientation of the image of the test chart; and identifying the plurality of quality test patterns in the located image of the test chart and evaluating the image quality of the plurality of quality test patterns.

Method of evaluating the quality of images

Described herein are a system and a method for abnormal behavior detection using automatic classification of multiple features. Features from various sources, including those extracted from camera input through digital image analysis, are used as input to machine learning algorithms. These algorithms group the features and produce models of normal and abnormal behaviors. Outlying behaviors, such as those identified by their lower frequency, are deemed abnormal. Human supervision may optionally be employed to ensure the accuracy of the models. Once created, these models can be used to automatically classify features as normal or abnormal. This invention is suitable for use in the automatic detection of abnormal traffic behavior such as running of red lights, driving in the wrong lane, or driving against traffic regulations.

Abnormal behavior detection system and method using automatic classification of multiple features

The invention discloses a correction method and system for object linking in video sequences that are captured by a multiple camera surveillance video system. The invention allows a user to select a specific object to track and for trace correction through an interactive user platform. The interactive user platform shows a video sequence captured by the user-selected camera at the selected time; lists of previous and post video sequences related to the user-selected object or video sequences, and lists of object linking results before and after the selected time. The user refers to the linking results, shown in lists of object linking results before and after the selected time to select one of objects for correction in selected frame of selected video sequence. The selection directs the proposed system to correct the trace of the specific object to track. Henceforth, the correct linking results can be automatically generated.

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Taipei, Taiwan

Yuang-Tzong Lan