Optimized Object Tracking Technique Using Kalman Filter
This is an incremental improvement for computer vision applications needing faster object tracking in real-time scenarios.
The paper tackled the problem of reducing processing time in object tracking while maintaining accuracy in cluttered scenes by using a Kalman filter with a cropped image window. The result showed that a window size of 2.16 times the object's largest dimension significantly sped up processing while achieving high detection success and accurate center positioning.
This paper focused on the design of an optimized object tracking technique which would minimize the processing time required in the object detection process while maintaining accuracy in detecting the desired moving object in a cluttered scene. A Kalman filter based cropped image is used for the image detection process as the processing time is significantly less to detect the object when a search window is used that is smaller than the entire video frame. This technique was tested with various sizes of the window in the cropping process. MATLAB was used to design and test the proposed method. This paper found that using a cropped image with 2.16 multiplied by the largest dimension of the object resulted in significantly faster processing time while still providing a high success rate of detection and a detected center of the object that was reasonably close to the actual center.