Looming Detection in Athletic Motion Using AI-Driven Visual Stimulus Modeling

Visual stimuli indicating rapid motion are critical to perceptual systems for initiating defensive or anticipatory responses. In high-speed sports such as tennis, detecting quickly moving objects across multiple viewpoints can provide valuable insight into attention, motor preparation, and reaction timing. This study presents a brain-inspired system for identifying motion events within video footage of tennis players, using motion dynamics to model visual patterns. Videos are processed through a custom pipeline that includes foreground segmentation, motion trajectory extraction, and event-based encoding. Detected stimuli are further analyzed for motion peaks and direction changes to infer perceptual salience. The system also supports detection of the tennis ball from multiple perspectives in real-world scenarios. Additionally, a stimulus generator is included to simulate expanding objects with structured backgrounds, enabling the creation of training data that reflects naturalistic conditions. Frames are transformed into dynamic vision sensor (DVS) representations, capturing positive and negative motion events for frame-by-frame analysis. The model aims to support real-time perceptual systems in both neuroscience research and applied human-computer interaction, offering a framework for tracking and interpreting complex motion cues in dynamic environments.