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Dynamic resource-aware corner detection for bio-inspired vision sensors

Mohamed, S. A. S. (författare)
Yasin, J. N. (författare)
Haghbayan, M. -H (författare)
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Miele, Antonio (författare)
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
Heikkonen, J. (författare)
Tenhunen, Hannu (författare)
KTH,Skolan för elektroteknik och datavetenskap (EECS)
Plosila, J. (författare)
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 (creator_code:org_t)
Institute of Electrical and Electronics Engineers (IEEE), 2020
2020
Engelska.
Ingår i: 2020 25th International Conference on Pattern Recognition, (ICPR). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 10465-10472
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
Stäng  
  • Event-based cameras are vision devices that transmit only brightness changes with low latency and ultra-low power consumption. Such characteristics make event-based cameras attractive in the field of localization and object tracking in resource-constrained systems. Since the number of generated events in such cameras is huge, the selection and filtering of the incoming events are beneficial from both increasing the accuracy of the features and reducing the computational load. In this paper, we present an algorithm to detect asynchronous corners form a stream of events in real-time on embedded systems. The algorithm is called the Three Layer Filtering-Harris or TLF-Harris algorithm. The algorithm is based on an events' filtering strategy whose purpose is 1) to increase the accuracy by deliberately eliminating some incoming events, i.e., noise and 2) to improve the real-time performance of the system, i.e., preserving a constant throughput in terms of input events per second, by discarding unnecessary events with a limited accuracy loss. An approximation of the Harris algorithm, in turn, is used to exploit its high-quality detection capability with a low-complexity implementation to enable seamless real-time performance on embedded computing platforms. The proposed algorithm is capable of selecting the best corner candidate among neighbors and achieves an average execution time savings of 59% compared with the conventional Harris score. Moreover, our approach outperforms the competing methods, such as eFAST, eHarris, and FA-Harris, in terms of real-time performance, and surpasses Arc* in terms of accuracy.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Nyckelord

Approximation algorithms
Biomimetics
Cameras
Edge detection
Embedded systems
Object tracking
Average Execution Time
Bio-inspired vision
Computational loads
Constrained systems
Embedded computing
Filtering strategies
Real time performance
Ultra-low power consumption
Real time systems

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