1 research outputs found
Real-time clustering and multi-target tracking using event-based sensors
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.Comment: Conference paper. Accepted for IROS 201