Pattern Recognition. ICPR International Workshops and Challenges
Virtual Event, January 10-15, 2021, Proceedings, Part VII
- 2021
- Book
- Editors
- Prof. Alberto Del Bimbo
- Prof. Rita Cucchiara
- Prof. Stan Sclaroff
- Dr. Giovanni Maria Farinella
- Tao Mei
- Prof. Dr. Marco Bertini
- Hugo Jair Escalante
- Dr. Roberto Vezzani
- Book Series
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
About this book
This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.
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Table of Contents
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Frontmatter
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PATCAST - International Workshop on Pattern Forecasting
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Frontmatter
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Adaptive Future Frame Prediction with Ensemble Network
Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi, Yoko SasakiThe chapter introduces an innovative adaptive future frame prediction framework using an ensemble network, designed to enhance video analysis by predicting future frames accurately. The proposed framework consists of three sub-networks: a pre-trained prediction network, a continuous-updating prediction network, and a weight estimation network. The pre-trained network is trained offline and provides stable predictions for similar scenes, while the continuous-updating network adapts to new environments in real-time. The weight estimation network blends the outputs of both networks to produce the final prediction. This ensemble approach ensures robust performance in dynamic and varied online environments. The authors also present a detailed network architecture and training process, demonstrating the effectiveness of their method through extensive experiments. The proposed framework outperforms existing state-of-the-art models in both offline and online settings, highlighting its potential for real-world applications in video analysis and prediction.AI Generated
This summary of the content was generated with the help of AI.
AbstractFuture frame prediction in videos is a challenging problem because videos include complicated movements and large appearance changes. Learning-based future frame prediction approaches have been proposed in kinds of literature. A common limitation of the existing learning-based approaches is a mismatch of training data and test data. In the future frame prediction task, we can obtain the ground truth data by just waiting for a few frames. It means we can update the prediction model online in the test phase. Then, we propose an adaptive update framework for the future frame prediction task. The proposed adaptive updating framework consists of a pre-trained prediction network, a continuous-updating prediction network, and a weight estimation network. We also show that our pre-trained prediction model achieves comparable performance to the existing state-of-the-art approaches. We demonstrate that our approach outperforms existing methods especially for dynamically changing scenes. -
Rain-Code Fusion: Code-to-Code ConvLSTM Forecasting Spatiotemporal Precipitation
Takato Yasuno, Akira Ishii, Masazumi AmakataThis chapter introduces 'Rain-Code Fusion,' a novel approach for spatiotemporal precipitation forecasting using ConvLSTM. The method fuses multi-frame rainy features to predict precipitation 6 hours ahead, essential for flood control in Japan. The study employs real-world data from 2006 to 2019, demonstrating the effectiveness of the 'Rain-Code' in reducing forecasting time and improving accuracy. The chapter also discusses the limitations of traditional methods and the potential of the 'Rain-Code' approach in extending forecasting ranges for dam inflow prediction and other weather-related applications.AI Generated
This summary of the content was generated with the help of AI.
AbstractRecently, flood damage has become a social problem owing to unexperienced weather conditions arising from climate change. An immediate response to heavy rain is important for the mitigation of economic losses and also for rapid recovery. Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 h forward for flood damage mitigation. However, the ordinary ConvLSTM has the limitation of predictable range more than 3-timesteps in real-world precipitation forecasting owing to the irreducible bias between target prediction and ground-truth value. This paper proposes a rain-code approach for spatiotemporal precipitation code-to-code forecasting. We propose a novel rainy feature that represents a temporal rainy process using multi-frame fusion for the timestep reduction. We perform rain-code studies with various term ranges based on the standard ConvLSTM. We applied to a dam region within the Japanese rainy term hourly precipitation data, under 2006 to 2019 approximately 127 thousands hours, every year from May to October. We apply the radar analysis hourly data on the central broader region with an area of 136 × 148 km2. Finally we have provided sensitivity studies between the rain-code size and hourly accuracy within the several forecasting range.
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- Title
- Pattern Recognition. ICPR International Workshops and Challenges
- Editors
-
Prof. Alberto Del Bimbo
Prof. Rita Cucchiara
Prof. Stan Sclaroff
Dr. Giovanni Maria Farinella
Tao Mei
Prof. Dr. Marco Bertini
Hugo Jair Escalante
Dr. Roberto Vezzani
- Copyright Year
- 2021
- Publisher
- Springer International Publishing
- Electronic ISBN
- 978-3-030-68787-8
- Print ISBN
- 978-3-030-68786-1
- DOI
- https://doi.org/10.1007/978-3-030-68787-8
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