Neuro-mathematical fusion for shot change detection in video sequences

DOI: 10.31673/2412-9070.2024.062091

Abstract

Shot change detection in visual media plays a pivotal role in various domains, including cinema, surveillance, and digital content organization. Traditional rule-based algorithms have shown limitations in handling the complexities of modern video content, prompting the exploration of computational intelligence approaches. This article presents a deep investigation of shot change detection, covering from traditional mathematical techniques to neural network methodologies. To test these approaches we decided to use the SHOT dataset which contains 853 short videos. This dataset provides a good variety of shot transitions that include difficult transitions like dissolve or zoom transitions that allow testing our approaches on modern-type videos. Through a series of experiments, we investigate the efficacy of a mathematical approach based on using various color spaces, histograms, and anomaly detection. Subsequently, we demonstrate the potential of integrating Long Short-Term Memory (LSTM) networks that replace the mathematical anomaly detection algorithm. Our findings reveal that combining mathematical precision with neural networks enhances shot change detection accuracy and efficiency, paving the way for practical real-time applications in the domain of video processing and analysis. These improvements underscore the importance of adaptability and innovation in addressing the evolving challenges of visual media processing while emphasizing the importance of ethical considerations in algorithmic decision-making processes. Overall, this article invites researchers to explore the intersection of mathematical rigor and neural networks in the realm of shot change detection, offering insights into future directions and opportunities in visual perception.
Keywords: shot change detection, neural networks, Long Short-Term Memory (LSTM), video content analysis, information processing, artificial intelligence.