Automatic construction of a recurrent neural network based classifier for vehicle passage detection

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur M.

doi:10.1117/12.2268706

Cited by 8 publications

(5 citation statements)

References 7 publications

(11 reference statements)

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“…Results of the evaluation of deep learning models trained on our synthetic datasets indicate that synthetic data can be efficiently used to improve performance and robustness of data-driven models in real-world resource-poor remote sensing applications. We could further increase overall computational efficiency thanks to sparse CNNs [47], detection accuracy by using approaches to utilizing multi-modal data [14], imbalanced classification [56,15] and a loss, tailored for change detection in sequences of events [16,9].…”

Section: Discussionmentioning

confidence: 99%

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

Kolos

Marin

Artemov

et al. 2019

Advances in Neural Networks – ISNN 2019

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Data-driven methods such as convolutional neural networks (CNNs) are known to deliver state-of-the-art performance on image recognition tasks when the training data are abundant. However, in some instances, such as change detection in remote sensing images, annotated data cannot be obtained in sufficient quantities. In this work, we propose a simple and efficient method for creating realistic targeted synthetic datasets in the remote sensing domain, leveraging the opportunities offered by game development engines. We provide a description of the pipeline for procedural geometry generation and rendering as well as an evaluation of the efficiency of produced datasets in a change detection scenario. Our evaluations demonstrate that our pipeline helps to improve the performance and convergence of deep learning models when the amount of real-world data is severely limited.

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Section: Discussionmentioning

confidence: 99%

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

Kolos

Marin

Artemov

et al. 2019

Advances in Neural Networks – ISNN 2019

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“…Future work provides for the more accurate data preprocessing and timeseries segmentation using hidden markov models [33] and anomaly detection approaches [34]. The online prediction of the player's performance shall be carried out using the specific metrics for classification of time-series segments [35] and manifold learning for nonlinear feature extraction [36].…”

Section: Discussionmentioning

confidence: 99%

eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair

Smerdov¹,

Burnaev²,

Somov³

2019

Preprint

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Today's competition between the professional eSports teams is so strong that in-depth analysis of players' performance literally crucial for creating a powerful team. There are two main approaches to such an estimation: obtaining features and metrics directly from the in-game data or collecting detailed information about the player including data on his/her physical training. While the correlation between the player's skill and in-game data has already been covered in many papers, there are very few works related to analysis of eSports athlete's skill through his/her physical behavior. We propose the smart chair platform which is to collect data on the person's behavior on the chair using an integrated accelerometer, a gyroscope and a magnetometer. We extract the important game events to define the players' physical reactions to them. The obtained data are used for training machine learning models in order to distinguish between the low-skilled and high-skilled players. We extract and figure out the key features during the game and discuss the results.

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“…earthquakes with big magnitude are rare events, a kind of anomalies. Thus we can first detect sequences of anomalies of different types in the historical stream of earthquake data [3,26,9,19,32], and then we can construct ensembles for rare events prediction [2,29] using detected anomalies and their features as precursors of major earthquakes to optimize specific detection metrics similar to the one used in [7], use privileged information about the future events, which is accessible during the training stage. Analogous approach, used in [8,28] for anomaly detection, allowed significant accuracy improvement, historical data on earthquakes has a spatial component, thus a graph of dependency between streams of events, registered by different ground stations can be constructed and modern methods for graph feature learning [20] and panel time-series feature extraction [24,23] ROC AUC score measures the quality of binary classifier.…”

Section: Discussionmentioning

confidence: 99%

Usage of Multiple RTL Features for Earthquakes Prediction

Proskura

Zaytsev

Braslavsky

et al. 2019

Computational Science and Its Applications – ICCSA 2019

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We construct a classification model that predicts if an earthquake with the magnitude above a threshold will take place at a given location in a time range 30-180 days from a given moment of time. A common approach is to use expert forecasts based on features like Region-Time-Length (RTL) characteristics. The proposed approach uses machine learning on top of multiple RTL features to take into account effects at various scales and to improve prediction accuracy. For historical data about Japan earthquakes 1992-2005 and predictions at locations given in this database the best model has precision up to ∼ 0.95 and recall up to ∼ 0.98.

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Automatic construction of a recurrent neural network based classifier for vehicle passage detection

Cited by 8 publications

References 7 publications

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair

Usage of Multiple RTL Features for Earthquakes Prediction

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