Fusing Appearance and Spatio-temporal Features for Multiple Camera Tracking

2014 ◽  
pp. 365-374 ◽  
Author(s):  
Nam Trung Pham ◽  
Karianto Leman ◽  
Richard Chang ◽  
Jie Zhang ◽  
Hee Lin Wang
Keyword(s):  
Camera Tracking ◽  
Multiple Camera ◽  
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

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.

scholarly journals High Speed Running and Sprinting Profiles of Elite Soccer Players

2017 ◽  
Vol 58 (1) ◽  
pp. 169-176 ◽  
Author(s):  
Javier Miñano-Espin ◽  
Luis Casáis ◽  
Carlos Lago-Peñas ◽  
Miguel Ángel Gómez-Ruano
Keyword(s):  
Physical Performance ◽  
High Intensity ◽  
Repeated Measures ◽  
High Speed ◽  
Tracking System ◽  
Camera Tracking ◽  
The Real ◽  
Multiple Camera ◽  
Repeated Measures Analysis

AbstractReal Madrid was named as the best club of the 20th century by the International Federation of Football History and Statistics. The aim of this study was to compare if players from Real Madrid covered shorter distances than players from the opposing team. One hundred and forty-nine matches including league, cup and UEFA Champions League matches played by the Real Madrid were monitored during the 2001-2002 to the 2006-2007 seasons. Data from both teams (Real Madrid and the opponent) were recorded. Altogether, 2082 physical performance profiles were examined, 1052 from the Real Madrid and 1031 from the opposing team (Central Defenders (CD) = 536, External Defenders (ED) = 491, Central Midfielders (CM) = 544, External Midfielders (EM) = 233, and Forwards (F) = 278). Match performance data were collected using a computerized multiple-camera tracking system (Amisco Pro®, Nice, France). A repeated measures analysis of variance (ANOVA) was performed for distances covered at different intensities (sprinting (>24.0 km/h) and high-speed running (21.1-24.0 km/h) and the number of sprints (21.1-24.0 km/h and >24.0 km/h) during games for each player sectioned under their positional roles. Players from Real Madrid covered shorter distances in high-speed running and sprint than players from the opposing team (p < 0.01). While ED did not show differences in their physical performance, CD (p < 0.05), CM (p < 0.01), EM (p < 0.01) and F (p > 0.01) from Real Madrid covered shorter distances in high-intensity running and sprint and performed less sprints than their counterparts. Finally, no differences were found in the high-intensity running and sprint distances performed by players from Real Madrid depending on the quality of the opposition.

scholarly journals Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features

2020 ◽  
Vol 17 (5) ◽  
pp. 4747-4772
Author(s):  
Faiz Ul Islam ◽  
◽  
Guangjie Liu ◽  
Weiwei Liu ◽  
Keyword(s):  
Temporal Features ◽  
Spatio Temporal
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