3D-FlowNet: Event-based optical flow estimation with 3D representation

Sun, Haixin; Dao, Minh-Quan; Frémont, Vincent

doi:10.1109/iv51971.2022.9827380

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…However, in practice, to ensure a proper signal-to-noise ratio, the illumination power and camera frame rate need to be controlled within the tissue safety limit [49]. Additionally, the resolution of image acquisition systems [50,51] and the stability of optical flow methods [52][53][54] can be enhanced, to extend the range of velocity measurement, and improve the accuracy of optical flow methods.…”

Section: Discussionmentioning

confidence: 99%

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

Meng

Huang

Feng

et al. 2023

IJMS

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The quantitative measurement of the microvascular blood-flow velocity is critical to the early diagnosis of microvascular dysfunction, yet there are several challenges with the current quantitative flow velocity imaging techniques for the microvasculature. Optical flow analysis allows for the quantitative imaging of the blood-flow velocity with a high spatial resolution, using the variation in pixel brightness between consecutive frames to trace the motion of red blood cells. However, the traditional optical flow algorithm usually suffers from strong noise from the background tissue, and a significant underestimation of the blood-flow speed in blood vessels, due to the errors in detecting the feature points in optical images. Here, we propose a temporal direction filtering and peak interpolation optical flow method (TPIOF) to suppress the background noise, and improve the accuracy of the blood-flow velocity estimation. In vitro phantom experiments and in vivo animal experiments were performed to validate the improvements in our new method.

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Section: Discussionmentioning

confidence: 99%

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

Meng

Huang

Feng

et al. 2023

IJMS

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“…-EV-FlowNet [117] by Zhu et al, and the studies by Liu et al [118], Nagata et al [119], and Sun et al [120] explore self-supervised learning approaches, addressing the challenge of sparse and asynchronous data through novel network architectures and loss functions.…”

Section: Categorymentioning

confidence: 99%

Event Cameras in Automotive Sensing: A Review

Shariff,

Dilmaghani,

Kielty

et al. 2024

IEEE Access

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Event cameras (EC) represent a paradigm shift and are emerging as valuable tools in the automotive industry, particularly for in-cabin and out-of-cabin monitoring. These cameras capture pixel intensity changes as "events" with ultra-low latency, making them suitable for real-time applications. In the context of in-cabin monitoring, EC offer solution for driver and passenger tracking, enhancing safety and comfort. For out-of-cabin monitoring, they excel in tracking objects and detecting potential hazards on the road. This article explores the applications, benefits, and challenges of event cameras in these two critical domains within the automotive industry. This review also highlights relevant datasets and methodologies, enabling researchers to make informed decisions tailored to their specific vehicular-technology and place their work in the broader context of EC sensing. Through an exploration of the hardware, the complexities of data processing, and customized algorithms for both in-cabin and out-of-cabin surveillance, this paper outlines a framework encompassing methodologies, tools, and datasets critical for the implementation of event camera sensing in automotive systems. INDEX TERMSNeuromorphic Event Camera, Event based Vision, Driver Monitoring System, Out-of-cabin Monitoring, In-Cabin Monitoring.

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“…As mentioned at the beginning of this paper, we based our solution on a traditional optical flow algorithm. However, the door is open to compare traditional methods with modern optical flow-based methods using deep learning, such as Full Flow [57], FlowNet 2.0 [58], LiteFlowNet [59], and 3D-FlowNet [60], among others.…”

Section: Comparisonmentioning

confidence: 99%

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Vera-Yanez,

Pereira,

Rodrigues

et al. 2024

Sensors

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The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.

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3D-FlowNet: Event-based optical flow estimation with 3D representation

Cited by 5 publications

References 16 publications

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

Event Cameras in Automotive Sensing: A Review

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

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