Road Region Detection by Spatio-Temporal Graph Segmentation of Optical Flows using On-Board Camera

2012 ◽  
Author(s):  
Kenji Nishida ◽  
Jun Fujiki ◽  
Takumi Kobayashi ◽  
Chikao Tsuchiya ◽  
Shinya Tanaka ◽  
...  
Keyword(s):  
Region Detection ◽  
Optical Flows ◽  
Temporal Graph ◽  
Spatio Temporal

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.

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

scholarly journals Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction

2021 ◽  
Vol 30 ◽  
pp. 7760-7775
Author(s):  
Maosen Li ◽  
Siheng Chen ◽  
Yangheng Zhao ◽  
Ya Zhang ◽  
Yanfeng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Motion Prediction ◽  
Temporal Graph ◽  
3D Skeleton ◽  
Spatio Temporal ◽  
Graph Neural Networks

scholarly journals Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling

2021 ◽  
pp. 125-134
Author(s):  
Ahmed El-Gazzar ◽  
Rajat Mani Thomas ◽  
Guido van Wingen
Keyword(s):  
Temporal Graph ◽  
Spatio Temporal

Data-Driven Time Series Forecasting for Social Studies Using Spatio-Temporal Graph Neural Networks

2021 ◽  
Author(s):  
Yi-Fan Li ◽  
Bo Dong ◽  
Latifur Khan ◽  
Bhavani Thuraisingham ◽  
Patrick T. Brandt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Social Studies ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Graph Neural Networks

Distributed and parallel processing for real-time and dynamic spatio-temporal graph

2019 ◽  
Vol 23 (2) ◽  
pp. 905-926
Author(s):  
Junhua Fang ◽  
Jiafeng Ding ◽  
Pengpeng Zhao ◽  
Jiajie Xu ◽  
An Liu ◽  
...  
Keyword(s):  
Parallel Processing ◽  
Real Time ◽  
Temporal Graph ◽  
Spatio Temporal
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