scholarly journals Video summarization from spatio-temporal features

2008 ◽  
Author(s):  
Robert Laganière ◽  
Raphael Bacco ◽  
Arnaud Hocevar ◽  
Patrick Lambert ◽  
Grégory Païs ◽  
...  
Keyword(s):  
Video Summarization ◽  
Temporal Features ◽  
Spatio Temporal

Fire Detection Based on Fractal Analysis and Spatio-Temporal Features

2021 ◽  
Author(s):  
Monir Torabian ◽  
Hossein Pourghassem ◽  
Homayoun Mahdavi-Nasab
Keyword(s):  
Fractal Analysis ◽  
Fire Detection ◽  
Temporal Features ◽  
Spatio Temporal

Video summarization via spatio-temporal deep architecture

2019 ◽  
Vol 332 ◽  
pp. 224-235 ◽  
Author(s):  
Sheng-hua Zhong ◽  
Jiaxin Wu ◽  
Jianmin Jiang
Keyword(s):  
Video Summarization ◽  
Deep Architecture ◽  
Spatio Temporal

Kinematic Adaptive Frequency Sampling Combined Spatio Temporal Features for Snow Monitoring in Aerospace Applications

2021 ◽  
pp. 115472
Author(s):  
Parameshwaran Ramalingam ◽  
Lakshminarayanan Gopalakrishnan ◽  
Manikandan Ramachandran ◽  
Rizwan Patan
Keyword(s):  
Aerospace Applications ◽  
Temporal Features ◽  
Frequency Sampling ◽  
Spatio Temporal

P4-225: Analysis of Spatio-Temporal Features of Histopathology in Murine Alzheimer's Disease Models and Human Alzheimer's Disease Samples

2016 ◽  
Vol 12 ◽  
pp. P1115-P1115
Author(s):  
Vera Niederkofler ◽  
Christina Hoeller ◽  
Joerg Neddens ◽  
Ewald Auer ◽  
Heinrich Roemer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Models ◽  
Temporal Features ◽  
Spatio Temporal

Predicting Human Mobility with Reinforcement-Learning-Based Long-Term Periodicity Modeling

2021 ◽  
Vol 12 (6) ◽  
pp. 1-23
Author(s):  
Shuo Tao ◽  
Jingang Jiang ◽  
Defu Lian ◽  
Kai Zheng ◽  
Enhong Chen
Keyword(s):  
Reinforcement Learning ◽  
Human Mobility ◽  
Recurrent Network ◽  
Mobility Prediction ◽  
Learning Framework ◽  
Temporal Features ◽  
Wide Range ◽  
Spatio Temporal ◽  
Historical Trajectory

Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically modeled; (2) most existing algorithms place attention mechanisms on top of recurrent network, so they can not allow for full parallelism and are inferior to self-attention for capturing long-range dependence; (3) most literature does not make good use of long-term historical information and do not effectively model the long-term periodicity of users. To this end, we propose MoveNet and RLMoveNet. MoveNet is a self-attention-based sequential model, predicting each user’s next destination based on her most recent visits and historical trajectory. MoveNet first introduces a cross-based learning framework for modeling feature interactions. With self-attention on both the most recent visits and historical trajectory, MoveNet can use an attention mechanism to capture the user’s long-term regularity in a more efficient way. Based on MoveNet, to model long-term periodicity more effectively, we add the reinforcement learning layer and named RLMoveNet. RLMoveNet regards the human mobility prediction as a reinforcement learning problem, using the reinforcement learning layer as the regularization part to drive the model to pay attention to the behavior with periodic actions, which can help us make the algorithm more effective. We evaluate both of them with three real-world mobility datasets. MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup. Moreover, RLMoveNet achieves higher prediction accuracy than MoveNet, which proves that modeling periodicity explicitly from the perspective of reinforcement learning is more effective.

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