Comparative Analysis of Movement Segmentation Techniques in Untrimmed Videos Using Optical Flow and Frame Differencing Using the $1 Unistroke Recognizer

Abstract

Untrimmed video analysis presents a complex challenge due to diverse movements across different domains, crucial for applications like action recognition, surveillance, and human-computer interaction. This study aims to compare the effectiveness of two main segmenting methods for untrimmed video segmentation and classification, addressing the challenge of diverse movements in various domains. Leveraging optical flow, frame differencing, and the 1$ algorithm, the performance of these methods in enabling robust and adaptable analysis of untrimmed video sequences is examined, facilitating accurate movement recognition across different contexts. Through a comparative study, the effectiveness and versatility of these segmenting methods are evaluated, revealing their capabilities to accurately segment and classify movements across different domains. The findings provide insights into the strengths and limitations of each method, offering implications for various fields, including computer vision, machine learning, and multimedia processing, and opening avenues for enhanced applications in diverse domains.