Abstract
![CDATA[Most successful computer vision models transform low-level features, such as Gabor filter responses, into richer representations of intermediate or mid-level complexity for downstream visual tasks. These mid-level representations have not been explored for event cameras, although it is especially relevant to the visually sparse and often disjoint spatial information in the event stream. By making use of locally consistent intermediate representations, termed as superevents, numerous visual tasks ranging from semantic segmentation, visual tracking, depth estimation shall benefit. In essence, superevents are perceptually consistent local units that delineate parts of an object in a scene. Inspired by recent deep learning architectures, we present a novel method that employs lifetime augmentation for obtaining an event stream representation that is fed to a fully convolutional network to extract superevents. Our qualitative and quantitative experimental results on several sequences of a benchmark dataset highlights the significant potential for event-based downstream applications.]]
Original language | English |
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Title of host publication | Proceedings of the 2021 International Conference on Neuromorphic Systems (ICONS 2021), Knoxville, TN, USA July 27 - 29, 2021 |
Publisher | Association for Computing Machinery |
Number of pages | 8 |
ISBN (Print) | 9781450386913 |
DOIs | |
Publication status | Published - 2021 |
Event | International Conference on Neuromorphic Systems - Duration: 27 Jul 2021 → … |
Conference
Conference | International Conference on Neuromorphic Systems |
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Period | 27/07/21 → … |