Spatial-Temporal Graph Convolutional Networks for Parking Space Prediction in Smart Cities

2021 ◽  
Author(s):  
Xiao Xiao ◽  
Zhiling Jin ◽  
Yilong Hui ◽  
Nan Cheng ◽  
Tom H. Luan
Keyword(s):  
Smart Cities ◽  
Parking Space ◽  
Convolutional Networks ◽  
Temporal Graph

scholarly journals Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction

2021 ◽  
Vol 13 (16) ◽  
pp. 3338
Author(s):  
Xiao Xiao ◽  
Zhiling Jin ◽  
Yilong Hui ◽  
Yueshen Xu ◽  
Wei Shao
Keyword(s):  
Smart Cities ◽  
Convolutional Networks ◽  
Temporal Correlations ◽  
Spatial Features ◽  
Temporal Features ◽  
Prediction Scheme ◽  
Temporal Graph ◽  
Real World Datasets ◽  
The Internet Of Things

With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called distAtt to measure the similarity of parking duration distributions. Through the distAtt mechanism, we add the long-term spatial–temporal correlations to our spatial–temporal convolutional block, and thus, we can capture complex hybrid spatial–temporal correlations to achieve a higher accuracy of parking availability prediction. Based on real-world datasets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the parking occupancy rate.

Two-Stream Spatial-Temporal Graph Convolutional Networks for Driver Drowsiness Detection

2021 ◽  
pp. 1-13
Author(s):  
Jing Bai ◽  
Wentao Yu ◽  
Zhu Xiao ◽  
Vincent Havyarimana ◽  
Amelia C. Regan ◽  
...  
Keyword(s):  
Drowsiness Detection ◽  
Convolutional Networks ◽  
Driver Drowsiness ◽  
Temporal Graph

scholarly journals STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

2020 ◽  
Vol 34 (02) ◽  
pp. 1342-1350 ◽  
Author(s):  
Uttaran Bhattacharya ◽  
Trisha Mittal ◽  
Rohan Chandra ◽  
Tanmay Randhavane ◽  
Aniket Bera ◽  
...  
Keyword(s):  
Real World ◽  
Classification Accuracy ◽  
Emotion Perception ◽  
Convolutional Network ◽  
Human Emotion ◽  
Convolutional Networks ◽  
Variational Autoencoder ◽  
Gait Features ◽  
Temporal Graph ◽  
Perceived Emotion

We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Given an RGB video of an individual walking, our formulation implicitly exploits the gait features to classify the perceived emotion of the human into one of four emotions: happy, sad, angry, or neutral. We train STEP on annotated real-world gait videos, augmented with annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE). We incorporate a novel push-pull regularization loss in the CVAE formulation of STEP-Gen to generate realistic gaits and improve the classification accuracy of STEP. We also release a novel dataset (E-Gait), which consists of 4,227 human gaits annotated with perceived emotions along with thousands of synthetic gaits. In practice, STEP can learn the affective features and exhibits classification accuracy of 88% on E-Gait, which is 14–30% more accurate over prior methods.

scholarly journals IoT Smart Parking System Based on the Visual-Aided Smart Vehicle Presence Sensor: SPIN-V

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1476 ◽  
Author(s):  
Luis F. Luque-Vega ◽  
David A. Michel-Torres ◽  
Emmanuel Lopez-Neri ◽  
Miriam A. Carlos-Mancilla ◽  
Luis E. González-Jiménez
Keyword(s):  
Smart Cities ◽  
Control Process ◽  
Parking Space ◽  
Parking System ◽  
Innovation And Technology ◽  
The Status ◽  
Working Together ◽  
Smart Parking ◽  
Vehicle Fleet ◽  
Smart Vehicle

Humanity is currently experiencing one of the short periods of transition thanks to novel sensing solutions for smart cities that bring the future to today. Overpopulation of cities demands the development of solid strategic plannings that uses infrastructure, innovation, and technology to adapt to rapid changes. To improve mobility in cities with a larger and larger vehicle fleet, a novel sensing solution that is the cornerstone of a smart parking system, the smart vehicular presence sensor (SPIN-V, in its Spanish abbreviation), is presented. The SPIN-V is composed of a small single-board computer, distance sensor, camera, LED indicator, buzzer, and battery and devoted to obtain the status of a parking space. This smart mobility project involves three main elements, namely the SPIN-V, a mobile application, and a monitoring center, working together to monitor, control, process, and display the parking space information in real-time to the drivers. In addition, the design and implementation of the three elements of the complete architecture are presented.

scholarly journals An Attention Enhanced Spatial–Temporal Graph Convolutional LSTM Network for Action Recognition in Karate

2021 ◽  
Vol 11 (18) ◽  
pp. 8641
Author(s):  
Jianping Guo ◽  
Hong Liu ◽  
Xi Li ◽  
Dahong Xu ◽  
Yihan Zhang
Keyword(s):  
Artificial Intelligence ◽  
Action Recognition ◽  
Structural Information ◽  
Human Action Recognition ◽  
Human Action ◽  
Competitive Sports ◽  
Convolutional Networks ◽  
Convolution Model ◽  
Artificial Intelligence Technology ◽  
Temporal Graph

With the increasing popularity of artificial intelligence applications, artificial intelligence technology has begun to be applied in competitive sports. These applications have promoted the improvement of athletes’ competitive ability, as well as the fitness of the masses. Human action recognition technology, based on deep learning, has gradually been applied to the analysis of the technical actions of competitive sports athletes, as well as the analysis of tactics. In this paper, a new graph convolution model is proposed. Delaunay’s partitioning algorithm was used to construct a new spatiotemporal topology which can effectively obtain the structural information and spatiotemporal features of athletes’ technical actions. At the same time, the attention mechanism was integrated into the model, and different weight coefficients were assigned to the joints, which significantly improved the accuracy of technical action recognition. First, a comparison between the current state-of-the-art methods was undertaken using the general datasets of Kinect and NTU-RGB + D. The performance of the new algorithm model was slightly improved in comparison to the general dataset. Then, the performance of our algorithm was compared with spatial temporal graph convolutional networks (ST-GCN) for the karate technique action dataset. We found that the accuracy of our algorithm was significantly improved.

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