scholarly journals Spike-FlowNet: Event-Based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks

2020 ◽  
pp. 366-382
Author(s):  
Chankyu Lee ◽  
Adarsh Kumar Kosta ◽  
Alex Zihao Zhu ◽  
Kenneth Chaney ◽  
Kostas Daniilidis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Flow ◽  
Energy Efficient ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Event Based ◽  
Hybrid Neural Networks

scholarly journals Optical Flow Estimation by Matching Time Surface with Event-Based Cameras

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1150
Author(s):  
Jun Nagata ◽  
Yusuke Sekikawa ◽  
Yoshimitsu Aoki
Keyword(s):  
Optical Flow ◽  
High Accuracy ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Optical Flows ◽  
Novel Method ◽  
Sensor Information ◽  
The One ◽  
Compensation Approach ◽  
Event Based

In this work, we propose a novel method of estimating optical flow from event-based cameras by matching the time surface of events. The proposed loss function measures the timestamp consistency between the time surface formed by the latest timestamp of each pixel and the one that is slightly shifted in time. This makes it possible to estimate dense optical flows with high accuracy without restoring luminance or additional sensor information. In the experiment, we show that the gradient was more correct and the loss landscape was more stable than the variance loss in the motion compensation approach. In addition, we show that the optical flow can be estimated with high accuracy by optimization with L1 smoothness regularization using publicly available datasets.

Optical flow estimation using channel attention mechanism and dilated convolutional neural networks

2019 ◽  
Vol 368 ◽  
pp. 124-132 ◽  
Author(s):  
Mingliang Zhai ◽  
Xuezhi Xiang ◽  
Rongfang Zhang ◽  
Ning Lv ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Neural Networks ◽  
Optical Flow ◽  
Convolutional Neural Networks ◽  
Attention Mechanism ◽  
Flow Estimation ◽  
Optical Flow Estimation

scholarly journals Traditional and modern strategies for optical flow: an investigation

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Syed Tafseer Haider Shah ◽  
Xiang Xuezhi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Optical Flow ◽  
Convolutional Neural Networks ◽  
Traditional Approach ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Advantages And Disadvantages ◽  
Recent Developments ◽  
Almost All

AbstractOptical Flow Estimation is an essential component for many image processing techniques. This field of research in computer vision has seen an amazing development in recent years. In particular, the introduction of Convolutional Neural Networks for optical flow estimation has shifted the paradigm of research from the classical traditional approach to deep learning side. At present, state of the art techniques for optical flow are based on convolutional neural networks and almost all top performing methods incorporate deep learning architectures in their schemes. This paper presents a brief analysis of optical flow estimation techniques and highlights most recent developments in this field. A comparison of the majority of pertinent traditional and deep learning methodologies has been undertaken resulting the detailed establishment of the respective advantages and disadvantages of the traditional and deep learning categories. An insight is provided into the significant factors that affect the success or failure of the two classes of optical flow estimation. In establishing the foremost existing and inherent challenges with traditional and deep learning schemes, probable solutions have been proposed indeed.

scholarly journals EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras

2018 ◽  
Author(s):  
Alex Zhu ◽  
Liangzhe Yuan ◽  
Kenneth Chaney ◽  
Kostas Daniilidis
Keyword(s):  
Optical Flow ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Event Based

An accurate optical flow estimation of PIV using fluid velocity decomposition

2021 ◽  
Vol 62 (4) ◽  
Author(s):  
Jin Lu ◽  
Hua Yang ◽  
Qinghu Zhang ◽  
Zhouping Yin
Keyword(s):  
Optical Flow ◽  
Fluid Velocity ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Velocity Decomposition

scholarly journals Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow

2020 ◽  
Vol 34 (07) ◽  
pp. 10713-10720
Author(s):  
Mingyu Ding ◽  
Zhe Wang ◽  
Bolei Zhou ◽  
Jianping Shi ◽  
Zhiwu Lu ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Segmentation ◽  
Video Clip ◽  
Semantic Segmentation ◽  
Temporal Consistency ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Optical Flows ◽  
Benchmark Datasets ◽  
Spatio Temporal

A major challenge for video semantic segmentation is the lack of labeled data. In most benchmark datasets, only one frame of a video clip is annotated, which makes most supervised methods fail to utilize information from the rest of the frames. To exploit the spatio-temporal information in videos, many previous works use pre-computed optical flows, which encode the temporal consistency to improve the video segmentation. However, the video segmentation and optical flow estimation are still considered as two separate tasks. In this paper, we propose a novel framework for joint video semantic segmentation and optical flow estimation. Semantic segmentation brings semantic information to handle occlusion for more robust optical flow estimation, while the non-occluded optical flow provides accurate pixel-level temporal correspondences to guarantee the temporal consistency of the segmentation. Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference. Extensive experiments show that the proposed model makes the video semantic segmentation and optical flow estimation benefit from each other and outperforms existing methods under the same settings in both tasks.

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