Optimized Object Tracking Technique Using Kalman Filter

TL;DR

This paper presents an optimized object tracking method using Kalman filters and image cropping to reduce processing time while maintaining detection accuracy in cluttered scenes.

Contribution

The paper introduces a novel object tracking approach combining Kalman filters with image cropping to improve speed without sacrificing detection accuracy.

Findings

Using a cropped image with 2.16 times the object size reduces processing time.

The method maintains high detection success rate.

Detected object centers are close to actual centers.

Abstract

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.