Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera

2025-10-08

AI Summary

This work addresses the challenge of real-time line segment detection and tracking in artificial environments using only a high-resolution event camera—without any frame-based camera assistance. Methodologically, we propose the first purely event-driven solution, featuring a lattice-allocated processing pipeline, a velocity-invariant event representation, a geometric-fitting-score-based line detection mechanism, and an endpoint dynamic perturbation tracking algorithm, all accelerated via parallelization for real-time performance. Experiments on both proprietary and public benchmarks demonstrate millisecond-level end-to-end latency and significantly improved detection and tracking accuracy over state-of-the-art event-frame hybrid and pure-event approaches. To our knowledge, this is the first method achieving robust, standalone geometric feature perception under high-dynamic scenes using events alone.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Tracking, profiling, and countermeasures against them

Abstract

Line segment extraction is effective for capturing geometric features of human-made environments. Event-based cameras, which asynchronously respond to contrast changes along edges, enable efficient extraction by reducing redundant data. However, recent methods often rely on additional frame cameras or struggle with high event rates. This research addresses real-time line segment detection and tracking using only a modern, high-resolution (i.e., high event rate) event-based camera. Our lattice-allocated pipeline consists of (i) velocity-invariant event representation, (ii) line segment detection based on a fitting score, (iii) and line segment tracking by perturbating endpoints. Evaluation using ad-hoc recorded dataset and public datasets demonstrates real-time performance and higher accuracy compared to state-of-the-art event-only and event-frame hybrid baselines, enabling fully stand-alone event camera operation in real-world settings.

Problem

• Real-time line detection using only event cameras
• Overcoming high event rates without frame cameras
• Achieving standalone operation in real-world environments

Innovation

• Velocity-invariant event representation for line detection
• Fitting score based line segment detection method
• Endpoint perturbation technique for line tracking