On event-based optical flow detection

Brosch, Tobias; Tschechne, Stephan; Neumann, Heiko

doi:10.3389/fnins.2015.00137

Cited by 89 publications

(87 citation statements)

References 64 publications

(113 reference statements)

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“…They are thus especially suitable for visual motion flow or optical flow (OF) computation (Benosman et al, 2014; Orchard and Etienne-Cummings, 2014; Brosch et al, 2015) along contours of objects. In the following sections, methods and mechanisms are proposed to estimate normal motion flows computed around events and to map them into a matrix in order to incrementally estimate scene motion distribution (locally or globally).…”

Section: Motion-based Featurementioning

confidence: 99%

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A Motion-Based Feature for Event-Based Pattern Recognition

et al. 2017

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This paper introduces an event-based luminance-free feature from the output of asynchronous event-based neuromorphic retinas. The feature consists in mapping the distribution of the optical flow along the contours of the moving objects in the visual scene into a matrix. Asynchronous event-based neuromorphic retinas are composed of autonomous pixels, each of them asynchronously generating “spiking” events that encode relative changes in pixels' illumination at high temporal resolutions. The optical flow is computed at each event, and is integrated locally or globally in a speed and direction coordinate frame based grid, using speed-tuned temporal kernels. The latter ensures that the resulting feature equitably represents the distribution of the normal motion along the current moving edges, whatever their respective dynamics. The usefulness and the generality of the proposed feature are demonstrated in pattern recognition applications: local corner detection and global gesture recognition.

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Section: Motion-based Featurementioning

confidence: 99%

“…More bio-inspired event-based OF computation methods such as Brosch et al (2015) and Orchard and Etienne-Cummings (2014) can be used but they are computationally more expensive.…”

Section: Motion-based Featurementioning

confidence: 99%

A Motion-Based Feature for Event-Based Pattern Recognition

et al. 2017

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“…First, in Ref. an approach is presented that is robust to singularities in u and v , which led to significant accuracy improvements in the comparison in Ref. .…”

Section: Event‐based Optical Flow Estimationmentioning

confidence: 99%

“…13,14 This novel approach to visual sensing is in general incompatible with state-of-the-art computer vision algorithms for estimating optical flow, due to the lack of absolute brightness measurements. Therefore, several event-based methods for optical flow estimation [15][16][17][18][19] as well as benchmarking datasets 20,21 have been developed. Of the existing techniques, the local plane-fitting approach of Ref.…”

Section: Introductionmentioning

confidence: 99%

Vertical landing for micro air vehicles using event‐based optical flow

Hordijk

Scheper

Croon

2017

Journal of Field Robotics

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Small flying robots can perform landing maneuvers using bio‐inspired optical flow by maintaining a constant divergence. However, optical flow is typically estimated from frame sequences recorded by standard miniature cameras. This requires processing full images onboard, which limits the update rate of divergence measurements and thus the speed of the control loop and the robot. Event‐based cameras overcome these limitations by only measuring pixel‐level brightness changes at microsecond temporal accuracy, hence providing an efficient mechanism for optical flow estimation. This paper presents, to the best of our knowledge, the first work integrating event‐based optical flow estimation into the control loop of a flying robot. We extend an existing “local plane fitting” algorithm to obtain an improved and more computationally efficient optical flow estimation method, which is valid for a wide range of optical flow velocities. This method is validated for real event sequences. In addition, a method for estimating the divergence from event‐based optical flow is introduced that accounts for the aperture problem. The developed algorithms are implemented in a constant divergence landing controller onboard a quadrotor. Experiments show that, using event‐based optical flow, accurate divergence estimates can be obtained over a wide range of speeds. This enables the quadrotor to perform very fast landing maneuvers.

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“…2). A similar approach was done in [30], and presents advantages compared to the inverse function theorem as suggested in [16]. To make the algorithm robust against noise, outliers need to be rejected.…”

Section: B From Events To Optic Flowmentioning

confidence: 99%

Bioinspired event-driven collision avoidance algorithm based on optic flow

Milde

Bertrand

Benosmanz

et al. 2015

2015 International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)

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Abstract-Any mobile agent, whether biological or robotic, needs to avoid collisions with obstacles. Insects, such as bees and flies, use optic flow to estimate the relative nearness to obstacles. Optic flow induced by ego-motion is composed of a translational and a rotational component. The segregation of both components is computationally and thus energetically expensive. Flies and bees actively separate the rotational and translational optic flow components via behaviour, i.e. by employing a saccadic strategy of flight and gaze control. Although robotic systems are able to mimic this gaze-strategy, the calculation of optic-flow fields from standard camera images remains time and energy consuming. To overcome this problem, we use a dynamic vision sensor (DVS), which provides event-based information about changes in contrast over time at each pixel location. To extract optic flow from this information, a plane-fitting algorithm estimating the relative velocity in a small spatio-temporal cuboid is used. The depthstructure is derived from the translational optic flow by using local properties of the retina. A collision avoidance direction is then computed from the event-based depth-structure of the environment. The system has successfully been tested on a robotic platform in open loop.

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On event-based optical flow detection

Cited by 89 publications

References 64 publications

A Motion-Based Feature for Event-Based Pattern Recognition

A Motion-Based Feature for Event-Based Pattern Recognition

Vertical landing for micro air vehicles using event‐based optical flow

Bioinspired event-driven collision avoidance algorithm based on optic flow

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