A method of recognising and tracking multiple solid objects in video sequences despite any kind of perspective
distortion is demonstrated. Moving objects are initially segmented from the scene using a background subtraction
method to minimize the search area of the filter. A variation on the Maximum Average Correlation Height (MACH)
filter is used to create invariance to orientation while giving high tolerance to background clutter and noise. A log r-θ
mapping is employed to give invariance to in-plane rotation and scale by transforming rotation and scale variations of the
target object into vertical and horizontal shifts. The MACH filter is trained on the log r-θ map of the target for a range of
orientations and applied sequentially over the regions of movement in successive video frames to test for target objects.
A Kalman filter is employed to continuously track the target objects over successive frames, which has enabled the
system to track multiple targets despite temporary occlusion or intersection.
A method of detecting target objects in still images despite any kind of geometrical distortion is demonstrated. Two existing techniques are combined, each one capable of creating invariance to various types of distortion of the target object. A maximum average correlation height (MACH) filter is used to create invariance to orientation and gives good tolerance to background clutter and noise. A log r- mapping is employed to give invariance to in-plane rotation and scale by transforming rotation and scale variations of the target object into vertical and horizontal shifts. The MACH filter is trained on the log r- map of the target for a range of orientations and applied sequentially over regions of interest in the input image. Areas producing a strong correlation response can then be used to determine the position, in-plane rotation, and scale of the target objects in the scene.
A method of tracking objects in video sequences despite any kind of perspective distortion is demonstrated. Moving objects are initially segmented from the scene using a background subtraction method to minimize the search area of the filter. A variation on the Maximum Average Correlation Height (MACH) filter is used to create invariance to orientation while giving high tolerance to background clutter and noise. A log r-θ mapping is employed to give invariance to in-plane rotation and scale by transforming rotation and scale variations of the target object into vertical and horizontal shifts. The MACH filter is trained on the log r-θ map of the target for a range of orientations and applied sequentially over the regions of movement in successive video frames. Areas of movement producing a strong
correlation response indicate an in-class target and can then be used to determine the position, in-plane rotation and scale of the target objects in the scene and track it over successive frames.
A method of detecting target objects in still images despite any kind of geometrical distortion is demonstrated. Two existing techniques are combined, each one capable of creating invariance to various types of distortion of the target object. A Maximum Average Correlation Height (MACH) filter is used to create invariance to orientation and gives good tolerance to background clutter and noise. A log r-θ mapping is employed to give invariance to in-plane rotation and scale by transforming rotation and scale variations of the target object into vertical and horizontal shifts. The MACH filter is trained on the log r-θ map of the target for a range of orientations and applied sequentially over regions of interest in the input image. Areas producing a strong correlation response can then be used to determine the position, in-plane rotation and scale of the target objects in the scene.
A method of detecting target objects in cluttered scenes despite any kind of geometrical distortion is demonstrated. Several existing techniques are combined, each one capable of creating invariance to one or more types of distortion of the target object. A MACH filter combined with an SDF creates invariance to orientation while constraining the correlation peak amplitudes and giving good tolerance to background clutter and noise. A log r-θ mapping is employed to give invariance to in-plane rotation and scale.
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