Supercharging Crowd Dynamics Estimation in Disasters via Spatio-Temporal Deep Neural Network

2017 ◽  
Author(s):  
Fang-Zhou Jiang ◽  
Lei Zhong ◽  
Kanchana Thilakarathna ◽  
Aruna Seneviratne ◽  
Kiyoshi Takano ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Crowd Dynamics ◽  
Spatio Temporal

Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting

2020 ◽  
Vol 11 (2) ◽  
pp. 571-583 ◽  
Author(s):  
Mahdi Khodayar ◽  
Saeed Mohammadi ◽  
Mohammad E. Khodayar ◽  
Jianhui Wang ◽  
Guangyi Liu
Keyword(s):  
Neural Network ◽  
Solar Irradiance ◽  
Deep Neural Network ◽  
Spatio Temporal

scholarly journals Spatio-temporal feature based deep neural network for cell lineage analysis in microscopy images

2021 ◽  
Author(s):  
Siteng Chen ◽  
Andrew L. Paek ◽  
Kathleen A. Lasick ◽  
Suvithanandhini Loganathan ◽  
Janet Roveda ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Spatial Distance ◽  
Feature Based ◽  
Spatio Temporal ◽  
Microscopy Images ◽  
Temporal Feature ◽  
Lineage Analysis

Background: Time-lapse microscopy has been widely used in biomedical experiments because it can visualize the molecular activities of living cells in real time. However, biomedical researchers are still conducting cell lineage analysis manually. Developing automatic lineage tracing algorithms is a challenging task. In the past two decades, deep neural networks (DNNs) became have shown outstanding performance on computer vision tasks. They can learn complex visual features, capture long-range temporal dependencies, and have the potential to be used for automatic cell lineage analysis. Methods: In this study, we propose a multi-task spatio-temporal feature based deep neural network for cell lineages analysis (Cell-STN). The Cell-STN extracts spatio-temporal features from microscopy image sequences by leveraging our convolutional long short-term memory based core block. And the proposed Cell-STN utilized a task specific network to predict the cell location, the mitosis event, and the apoptosis event in a multi-task manner. Results: We evaluated the Cell-STN on three in-house datasets (MCF7, U2OS, and HCT116) and one public dataset (Fluo-N2DL-HeLa). For cell tracking, we used peak-wise precision, track-wise precision, end-peak precision, and spatial distance as metrics. The overall results showed the Cell-STN models outperform other state-of-the-art cell trackers. For mitosis and apoptosis tasks, we used accuracy, F1-score, temporal distance, and spatial distance as metrics. The Cell-STN models achieved the highest performance on all datasets. Conclusion: This study presented a novel DNNs approach for cell lineage analysis in microscopy images. The Cell-STN showed outstanding performance on the four datasets. Additionally, the Cell-STN required minimal training data and can be adapted to new biological event detection tasks by appending task-specific layers. This algorithm has the potential to be used in real-world biomedical research.

Semantic Scene Mapping with Spatio-temporal Deep Neural Network for Robotic Applications

2017 ◽  
Vol 10 (2) ◽  
pp. 260-271 ◽  
Author(s):  
Ruihao Li ◽  
Dongbing Gu ◽  
Qiang Liu ◽  
Zhiqiang Long ◽  
Huosheng Hu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Spatio Temporal ◽  
Semantic Scene ◽  
Robotic Applications

scholarly journals Spatio-Temporal Action Detection in Untrimmed Videos by Using Multimodal Features and Region Proposals

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1085 ◽  
Author(s):  
Yeongtaek Song ◽  
Incheol Kim
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Temporal Region ◽  
Action Detection ◽  
Coarse Level ◽  
Proposed Model ◽  
Spatio Temporal ◽  
Temporal Action

This paper proposes a novel deep neural network model for solving the spatio-temporal-action-detection problem, by localizing all multiple-action regions and classifying the corresponding actions in an untrimmed video. The proposed model uses a spatio-temporal region proposal method to effectively detect multiple-action regions. First, in the temporal region proposal, anchor boxes were generated by targeting regions expected to potentially contain actions. Unlike the conventional temporal region proposal methods, the proposed method uses a complementary two-stage method to effectively detect the temporal regions of the respective actions occurring asynchronously. In addition, to detect a principal agent performing an action among the people appearing in a video, the spatial region proposal process was used. Further, coarse-level features contain comprehensive information of the whole video and have been frequently used in conventional action-detection studies. However, they cannot provide detailed information of each person performing an action in a video. In order to overcome the limitation of coarse-level features, the proposed model additionally learns fine-level features from the proposed action tubes in the video. Various experiments conducted using the LIRIS-HARL and UCF-10 datasets confirm the high performance and effectiveness of the proposed deep neural network model.

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