Angus Apps

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About

Angus Apps is a pioneering researcher in robotics and computer vision, with a primary focus on event-based perception for dynamic environments. His work centers on leveraging the unique capabilities of event cameras—their low latency, high temporal resolution, and high dynamic range—to enable robots to perceive and interact with fast-moving scenes where traditional frame-based sensors fail. Apps’s most notable contribution is the Asynchronous Event Multi-Object Tracking (AEMOT) algorithm, a novel approach that detects and tracks multiple objects in real time using event data alone. This work, published in 2025, has already garnered early citations, signaling its potential to reshape autonomous navigation and surveillance systems. By addressing the challenge of asynchronous, high-speed object tracking, Apps is pushing the boundaries of how robots can operate safely in unpredictable, high-motion scenarios. His research holds promise for applications in drone swarms, autonomous driving, and industrial automation, where split-second decisions are critical. With a growing citation footprint, Angus Apps is establishing himself as a rising leader in event-based vision and robotic perception.