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- W3205719872 abstract "In this paper, we propose a novel approach called H2RP (2D Histogram based Region Proposal) for efficient region proposal on AER (Address Event Representation) data generated by commercial neuromorphic vision sensors on a stationary setup. The proposed H2RP technique is based upon 2D histogram event-count images. We show that proposed H2RP has low computational footprint and no memory overhead compared to other state of the art region proposal techniques for neuromorphic vision data. Proposed technique derives its high computational efficiency by eliminating explicit requirement of dedicated noise filtering. To validate the proposed technique we benchmark it on two different real world AER datasets generated from two different commercial event sensors. Datasets used for this study constitute the (i) DAVIS-traffic surveillance dataset, and (ii) specially recorded moving object dataset. Our pipeline including H2RP along with an overlap tracker/kalman filter shows a gain of ∼ 87 × in terms of computational footprint compared to other state of the art algorithms. Further, we incorporate a light CNN for object recognition. We also evaluate the proposed pipeline on two different edge-hardware (Jetson Nano, Raspberry Pi 3B +) platforms." @default.
- W3205719872 created "2021-10-25" @default.
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- W3205719872 date "2021-07-27" @default.
- W3205719872 modified "2023-09-25" @default.
- W3205719872 title "2D Histogram based Region Proposal (H2RP) Algorithm for Neuromorphic Vision Sensors" @default.
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- W3205719872 doi "https://doi.org/10.1145/3477145.3477263" @default.
- W3205719872 hasPublicationYear "2021" @default.
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