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- W2813817099 abstract "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." @default.
- W2813817099 created "2018-07-19" @default.
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- W2813817099 date "2018-10-01" @default.
- W2813817099 modified "2023-09-25" @default.
- W2813817099 title "Real-Time Clustering and Multi-Target Tracking Using Event-Based Sensors" @default.
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- W2813817099 doi "https://doi.org/10.1109/iros.2018.8593380" @default.
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