vSTMD: Visual Motion Detection for Extremely Tiny Target at Various Velocities

TL;DR

This paper introduces vSTMD, a novel, learning-free visual motion detection model for tiny targets across various velocities, outperforming existing methods in accuracy and speed, suitable for real-time applications.

Contribution

The paper presents vSTMD, a new natural architecture with a self-adaptive mechanism and efficient gradient calculation, enabling robust detection of tiny targets at diverse velocities without training.

Findings

vSTMD achieves 30% higher F1 score than SOTA models.

vSTMD-F improves F1 score by 58% over previous approaches.

The model is 60 times faster than deep learning methods.

Abstract

Visual motion detection for extremely tiny (ET-) targets is challenging, due to their category-independent nature and the scarcity of visual cues, which often incapacitate mainstream feature-based models. Natural architectures with rich interpretability offer a promising alternative, where STMD architectures derived from insect visual STMD (Small Target Motion Detector) pathways have demonstrated their effectiveness. However, previous STMD models are constrained to a narrow velocity range, hindering their efficacy in real-world scenarios where targets exhibit diverse and unstable dynamics. To address this limitation, we present vSTMD, a learning-free model for motion detection of ET-targets at various velocities. Our key innovations include: (1) a cross-Inhibition Dynamic Potential (cIDP) that serves as a self-adaptive mechanism efficiently capturing motion cues across a wide velocity spectrum, and (2) the first Collaborative Directional Gradient Calculation (CDGC) strategy, which enhances orienting accuracy and robustness while reducing computational overhead to one-eighth of previously isolated strategies. Evaluated on the real-world dataset RIST, the proposed vSTMD and its feedback-facilitated variant vSTMD-F achieve relative $F_{1}$ gains of $30\%$ and $58\%$ over state-of-the-art (SOTA) STMD approaches, respectively. Furthermore, both models demonstrate competitive orientation estimation performance compared to SOTA deep learning-driven methods. Experiments also reveal the superiority of the natural architecture for ET-object motion detection - vSTMD is $60\times$ faster than contemporary data-driven methods, making it highly suitable for real-time applications in dynamic scenarios and complex backgrounds. Code is available at https://github.com/MingshuoXu/vSTMD.