Data-set for Event-based Optical Flow Evaluation in Robotics Applications

Khairallah, Mahmoud Z.; Bonardi, Fabien; Roussel, David; Bouchafa, Samia

doi:10.5220/0010320304800489

Cited by 2 publications

(3 citation statements)

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“…To validate the reliability and accuracy of the algorithm we propose, we used the data-sets provided previously [8,10]. We used two sequences of the first data-set which cover translational and rotational dominant motion and the data set of moving stripes to validate the estimated lifetime.…”

Section: Resultsmentioning

confidence: 99%

“…We used two sequences of the first data-set which cover translational and rotational dominant motion and the data set of moving stripes to validate the estimated lifetime. In [8], a VICON system is used to track the motion of multiple objects and 3D/2D projection is used to create optical flow ground truth (see Figure 6). These sequences are recorded using an ATIS (Asynchronous Time-based Image Sensor) camera [12] provided by PROPHESEE with a 480 × 360 resolution which can provide change detection, events detection and exposure measurements (see Figure 1).…”

Section: Resultsmentioning

confidence: 99%

“…These values are chosen based on practical experiments. To make sure that all events belong to real motion, an adaptive activity filter [8] is applied. Adaptive filtering in this context consists of storing 8-pixel neighborhood events in a buffer and checking whenever a new event is triggered.…”

Section: Events Filteringmentioning

confidence: 99%

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PCA Event-Based Optical Flow for Visual Odometry

Khairallah¹,

Bonardi²,

Roussel³

et al. 2021

Preprint

Self Cite

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With the advent of neuromorphic vision sensors such as event-based cameras, a paradigm shift is required for most computer vision algorithms. Among these algorithms, optical flow estimation is a prime candidate for this process considering that it is linked to a neuromorphic vision approach. Usage of optical flow is widespread in robotics applications due to its richness and accuracy. We present a Principal Component Analysis (PCA) approach to the problem of event-based optical flow estimation. In this approach, we examine different regularization methods which efficiently enhance the estimation of the optical flow. We show that the best variant of our proposed method, dedicated to the real-time context of visual odometry, is about two times faster compared to state-of-the-art implementations while significantly improves optical flow accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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PCA Event-Based Optical Flow for Visual Odometry

Khairallah¹,

Bonardi²,

Roussel³

et al. 2021

Preprint

Self Cite

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PCA Event-Based Optical Flow: A Fast and Accurate 2D Motion Estimation

Khairallah

Bonardi

Roussel

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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For neuromorphic vision sensors such as event-based cameras, a paradigm shift is required to adapt optical flow estimation as it is critical for many applications. Regarding the costly computations, Principal Component Analysis (PCA) approach is adapted to the problem of event-based optical flow estimation. We propose different PCA regularization methods enhancing the optical flow estimation efficiently. Furthermore, we show that the variants of our proposed method, dedicated to real-time context, are about two times faster than state-of-the-art implementations while significantly improving optical flow accuracy.

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Data-set for Event-based Optical Flow Evaluation in Robotics Applications

Cited by 2 publications

References 0 publications

PCA Event-Based Optical Flow for Visual Odometry

PCA Event-Based Optical Flow for Visual Odometry

PCA Event-Based Optical Flow: A Fast and Accurate 2D Motion Estimation

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