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Asynchronous, Photometric Feature Tracking Using Events and Frames


Gehrig, Daniel; Rebecq, Henri; Gallego, Guillermo; Scaramuzza, Davide (2018). Asynchronous, Photometric Feature Tracking Using Events and Frames. In: Ferrari, Vittorio; Hebert, Martial; Sminchisescu, Cristian; Weiss, Yair. Computer Vision – ECCV 2018. Cham: Springer, 766-781.

Abstract

We present a method that leverages the complementarity of event cameras and standard cameras to track visual features with lowlatency. Event cameras are novel sensors that output pixel-level brightness changes, called \events". They oer signicant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the same scene pattern can produce dierent events depending on the motion direction, establishing event correspondences across time is challenging. By contrast, standard cameras provide intensity measurements (frames) that do not depend on motion direction. Our method extracts features on frames and subsequently tracks them asynchronously using events, thereby exploiting the best of both types of data: the frames provide a photometric representation that does not depend on motion direction and the events provide low-latency updates. In contrast to previous works, which are based on heuristics, this is the rst principled method that uses raw intensity measurements directly, based on a generative event model within a maximum-likelihood framework. As a result, our method produces feature tracks that are both more accurate (subpixel accuracy) and longer than the state of the art, across a wide variety of scenes.

Abstract

We present a method that leverages the complementarity of event cameras and standard cameras to track visual features with lowlatency. Event cameras are novel sensors that output pixel-level brightness changes, called \events". They oer signicant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the same scene pattern can produce dierent events depending on the motion direction, establishing event correspondences across time is challenging. By contrast, standard cameras provide intensity measurements (frames) that do not depend on motion direction. Our method extracts features on frames and subsequently tracks them asynchronously using events, thereby exploiting the best of both types of data: the frames provide a photometric representation that does not depend on motion direction and the events provide low-latency updates. In contrast to previous works, which are based on heuristics, this is the rst principled method that uses raw intensity measurements directly, based on a generative event model within a maximum-likelihood framework. As a result, our method produces feature tracks that are both more accurate (subpixel accuracy) and longer than the state of the art, across a wide variety of scenes.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:German
Date:2018
Deposited On:31 Oct 2019 09:54
Last Modified:31 Oct 2019 09:55
Publisher:Springer
Number:11216
ISBN:978-3-030-01257-1
Additional Information:978-3-030-01258-8 (E)
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-3-030-01258-8_46
Official URL:http://rpg.ifi.uzh.ch/docs/ECCV18_Gehrig.pdf
Other Identification Number:merlin-id:18692

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