An Effective Motion-Tracking Scheme for Machine-Learning Applications in Noisy Videos

Abstract

Detecting and tracking objects of interest in videos is a technology that can be used in various applications. For example, identifying cell movements or mutations through videos obtained in real time can be useful information for decision making in the medical field. However, depending on the situation, the quality of the video may be below the expected level, and in this case, it may be difficult to check necessary information. To overcome this problem, we proposed a technique to effectively track objects by modifying the simplest color balance (SCB) technique. An optimal object detection method was devised by mixing the modified SCB algorithm and a binarization technique. We presented a method of displaying object labels on a per-frame basis to track object movements in a video. Detecting objects and tagging labels through this method can be used to generate object motion-based prediction training data for machine learning. That is, based on the generated training data, it is possible to implement an artificial intelligence model for an expert system based on various object motion measurements. As a result, the main object detection accuracy in noisy videos was more than 95%. This method also reduced the tracking loss rate to less than 10%.