Abstract:
An object attribution analyzing method applied to an object attribution analyzing device and includes dividing a plurality of continuous frames into a current frame and several previous frames, utilizing face detection to track and compute a first attribution predicted value of an object within the current frame, utilizing the face detection to acquire a feature parameter of the object within the current frame for setting a first weighting, acquiring a second attribution predicted value of the object within the several previous frames, setting a second weighting in accordance with the first weighting, and generating a first induction attribution predicted value of the object within the plurality of continuous frames via the first attribution predicted value weighted by the first weighting and the second attribution predicted value weighted by the second weighting.
Abstract:
An object attribution analyzing method applied to an object attribution analyzing device and includes dividing a plurality of continuous frames into a current frame and several previous frames, utilizing face detection to track and compute a first attribution predicted value of an object within the current frame, utilizing the face detection to acquire a feature parameter of the object within the current frame for setting a first weighting, acquiring a second attribution predicted value of the object within the several previous frames, setting a second weighting in accordance with the first weighting, and generating a first induction attribution predicted value of the object within the plurality of continuous frames via the first attribution predicted value weighted by the first weighting and the second attribution predicted value weighted by the second weighting.
Abstract:
A stereo vision image calibration method is applied to an image capturing device having a first camera, a second camera and an image rectifying function. The first camera and the second camera are arranged along a first direction. The first camera and the second camera are respectively utilized to acquire a first image and a second image. The image rectifying function rectifies the first image and the second image by a stereo vision parameter. The stereo image calibration method includes selecting the rectified first image and the rectified second image to compute a global disparity vector between the adjust image and the reference image, and if a vector component of the global disparity vector along a second direction which is different from the first direction has non-zero value, processing a stereo vision calibration according to the vector component.
Abstract:
A pedestrian detection method is applied to a monitoring camera. The pedestrian detection method includes forming a first detecting window on at least one monitoring frame via an object analysis function, utilizing a human form detection function to modulate the first detecting window for forming a second detecting window, analyzing the second detecting window via a human local detection function to mark an upper detecting window about a pedestrian on the monitoring frame, and determining whether to calibrate the second detecting window via analysis of the upper detecting window.
Abstract:
An automatic calibration system applied to a camera and a related automatic calibration method are disclosed. The automatic calibration method utilizes a motionless calibration plate to calculate a calibration parameter of the camera, the camera includes at least one image sensing unit, and the camera is assembled with a testing device. The automatic calibration method includes rotating the camera by a first shaft and a second shaft of the testing device to change an angle of the at least one image sensing unit of the camera toward the calibration plate, capturing a plurality of images containing the calibration plate by the camera while rotating, calculating the calibration parameter of the camera according to the images, and storing the calibration parameter.
Abstract:
A pedestrian detection method is applied to a monitoring camera. The pedestrian detection method includes forming a first detecting window on at least one monitoring frame via an object analysis function, utilizing a human form detection function to modulate the first detecting window for forming a second detecting window, analyzing the second detecting window via a human local detection function to mark an upper detecting window about a pedestrian on the monitoring frame, and determining whether to calibrate the second detecting window via analysis of the upper detecting window.
Abstract:
A stereo vision image calibration method is applied to an image capturing device having a first image capturing unit, a second image capturing unit and an image rectify unit. The first image capturing unit and the second image capturing unit are arranged along a first direction. The first image capturing unit and the second image capturing unit are respectively utilized to acquire a first image and a second image. The image rectify unit rectifies the first image and the second image by a stereo vision parameter. The stereo image calibration method includes selecting the rectified first image and the rectified second image to compute a global disparity vector between the adjust image and the reference image, and if a vector component of the global disparity vector along a second direction which is different from the first direction has non-zero value, processing a stereo vision calibration according to the vector component.