2018/07/08 by Francisco Barranco, Barranco, Francisco, Cornelia Fermüller +3 · 1 citation
Computer Science · Engineering · #Advanced Memory and Neural Computing #Computer Vision and Pattern Recognition (cs.CV) #Distributed Control Multi-Agent Systems #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1807.02851
openalex publication_date 2018/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Clustering is crucial for many computer vision applications such as robust tracking, object detection and segmentation. This work presents a real-time clustering technique that takes advantage of the unique properties of event-based vision sensors. Since event-based sensors trigger events only when the intensity changes, the data is sparse, with low redundancy. Thus, our approach redefines the well-known mean-shift clustering method using asynchronous events instead of conventional frames. The potential of our approach is demonstrated in a multi-target tracking application using Kalman filters to smooth the trajectories. We evaluated our method on an existing dataset with patterns of different shapes and speeds, and a new dataset that we collected. The sensor was attached to the Baxter robot in an eye-in-hand setup monitoring real-world objects in an action manipulation task. Clustering accuracy achieved an F-measure of 0.95, reducing the computational cost by 88% compared to the frame-based method. The average error for tracking was 2.5 pixels and the clustering achieved a consistent number of clusters along time.