Probabilistic Triangulation for Uncalibrated Multi-View 3D Human Pose Estimation

3D human pose estimation has been a long-standing challenge in computer vision and graphics, where multi-view methods have significantly progressed but are limited by the tedious calibration processes. Existing multi-view methods are restricted to fixed camera pose and therefore lack generalization ability. This paper presents a novel Probabilistic Triangulation module that can be embedded in a calibrated 3D human pose estimation method, generalizing it to uncalibration scenes. The key idea is to use a probability distribution to model the camera pose and iteratively update the distribution from 2D features instead of using camera pose. Specifically, We maintain a camera pose distribution and then iteratively update this distribution by computing the posterior probability of the camera pose through Monte Carlo sampling. This way, the gradients can be directly back-propagated from the 3D pose estimation to the 2D heatmap, enabling end-to-end training. Extensive experiments on Human3.6M and CMU Panoptic demonstrate that our method outperforms other uncalibration methods and achieves comparable results with state-of-the-art calibration methods. Thus, our method achieves a trade-off between estimation accuracy and generalizability. Our code is in https://github.com/bymaths/probabilistic_triangulationComment: 9pages, 5figures, conferenc

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nodes to the model in a meaningful order, from easy to complex, can provide performance improvements over traditional training procedure. The idea has its root in Curriculum Learning which suggests in the early stage of training models can be sensitive to noise and difficult samples. In this paper, we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for spatial-temporal graph modeling. Specifically, we evaluate the learning difficulty of each node in high-level feature space and drop those difficult ones out to ensure the model only needs to handle fundamental ST relations at the beginning, before gradually moving to hard ones. Our strategy can be applied to any canonical deep learning architecture without extra trainable parameters, and extensive experiments on a wide range of datasets are conducted to illustrate that, by controlling the difficulty level of ST relations as the training progresses, the model is able to capture better representation of the data and thus yields better generalization

The Preservation of Door Gods in Traditional Taiwanese Temples

[EN] This paper focuses on the study and conservation of the gods painted on the doors of traditional temples in Taiwan. These paintings are continually exposed to poor environmental conditions (especially sunlight, rain, and pollution) and human factors, such as continuous ritual activities. After reviewing the technical characteristics of these paintings and their origins, traditional views and contemporary practices followed in the restoration of temples are explored. Since preventive conservation is a key issue in the preservation of cultural heritage, some solutions that have already been carried out, as well as suggestions for others that could be put into practice in order to improve the situation and extend the life expectancy of these paintings, are considered. Finally, while it is inevitable to try to preserve some of the most outstanding pieces, the possibility of considering these works as ephemeral is contemplated. This may seem contradictory, but it is, in fact, a relatively common situation when addressing the conservation of religious heritage in use. Undoubtedly, the preservation of this heritage still raises many questions and exposes a number of contradictions.Wu, W.; Barros García, JM. (2020). The Preservation of Door Gods in Traditional Taiwanese Temples. Studies in Conservation. 65(8):475-486. https://doi.org/10.1080/00393630.2020.1712110S475486658Clart, P., & Jones, C. B. (Eds.). (2003). Religion in Modern Taiwan. doi:10.1515/9780824845063Ferrazza, L., and D. Juanes Barber. 2014. Informe preliminar: análisis de la pintura sobre tabla de dos puertas orientales (N° de registro: 233/2014). Subdirección de Conservación, Restauración e Investigación IVC + R de CulturArts Generalitat Valenciana (unpublished).ICOMOS. 2013. The Burra Charter: The Australia ICOMOS Charter for Places of Cultural Significance. https://australia.icomos.org/publications/charters/.Li, L.F. 2004. “An Introduction to the Study of the Conservation and Restoration of Monument III- Xingji Temple’s Paintings in Tainan [台南市三級古蹟興濟宮建築彩繪保存修護研究案例介紹].” In 2004 Congress on the Conservation and Restoration of Building Paintings [年建築彩繪保存修護研習]. December 12–26, 2004. Tainan [in Chinese].Li, H.Y. 2012. Lectures on the Deterioration of Wood Structures and the Prevention and Control of Insects in Historical Buildings [古蹟歷史建築木構造生物劣化與蟲蟻防治教育研習講座]. [in Chinese].Li, L.F., M.S. Zheng, and Y.L. Cai. 2008. “The Current State of Conservation and Preservation of the Architectural Paintings of Taiwanese Temples [台灣寺廟建築彩繪保存維護現況].” In Congress on the Conservation and Preservation of Paintings on Wood in East Asian Architecture [東亞木構建築彩繪保存維護研討會]. March 14, 2008. Tainan [in Chinese].Pan, H. 2004. “The Study on the Basement Materials Coated for Architectural Paints. The Contemporary Ones in Taiwan as an Example [建築彩繪地仗層之研究-以台灣當代作法為例.” Master Diss., National Cheng Kung University, Tainan [in Chinese]. http://ir.lib.ncku.edu.tw/handle/987654321/27772.Tang, Y.F. 2006. “A Study on Thinking of the Temple Paintings Conservation in Taiwan [台灣寺廟彩畫維護思維之研究].” Master Diss., Shu-Te University, Kaohsiung [in Chinese].Tseng, Y., Wu, C., Juan, C., Wang, S., Li, Z., Kuo, K., … Wu, W. (2014). Conservation of polychrome paintings in Tien-hou Kung, Penghu, Taiwan. Studies in Conservation, 59(sup1), S271-S272. doi:10.1179/204705814x13975704320837Tung, Y.Y., and S. Hsieh. 2010. “Exploring the Approach to the Conservation and Restoration of Taiwan’s Traditional Temple Artifacts.” In Multidisciplinary Conservation: A Holistic View for Historic Interiors. ICOM-CC Interim Meeting, Rome. https://www.icom-cc.org/54/document/exploring-the-approach-to-the-conservation-and-restoration-of-taiwans-traditional-temple-artifacts/?id=862.Wu, W. 2016. “Estudio y Conservación de las Pinturas de los Dioses, Realizadas por el Pintor Cai Cao-Ru, en las Puertas de los Templos de Taiwan.” PhD diss., Universitat Politècnica de València, Valencia. https://hdl.handle.net/10251/61040.Xu, M.F. 2003. “Conservation and Restoration of Paintings in Traditional Taiwanese Temples Considered Monuments: The Case of Tainan [由南瀛的案例來談台灣傳統寺廟古蹟彩畫的保存與修復].” The Landscape of Humanism: Presentations at the Tainan Traditional Art Seminar [南瀛人文景觀: 南瀛傳統藝術硏討會論文集]. Yilan: National Center for Traditional Arts [in Chinese].Xue, Q. 1997. “Techniques for the Restoration of Paintings in Traditional Constructions [傳統建築彩繪修護技術].” Traditional Art Seminar 1997 [年傳統藝術研討會論文集]. Taipei: Taipei National University of the Arts, Center for Traditional Arts [in Chinese]

Imputation of Missing Traffic Flow Data by Using Denoising Autoencoders

In transportation engineering, Spatio-temporal data including traffic flow, speed, and occupancy are collected from different kinds of sensors and used by transportation engineers for analysis. However, the missing data influence the analysis and prediction results significantly. In this thesis, Denoising Autoencoders are used to impute the missing traffic flow data. First, we focused on the general situation and used three kinds of Denoising Autoencoders: “Vanilla”, CNN, and Bi-LSTM to implement the data with a general missing rate of 30%. Each model was optimized by focusing on the main hyper-parameters since the tuning can influence the accuracy of the final prediction result. Then, the Autoencoder models are used to train and test data with an exceptionally high missing rate of about 80%. We do this to test and then demonstrate that even under extreme loss conditions, Autoencoder models are very robust. By observing the hyper-parameter tuning process, the changing prediction accuracy is shown and in most cases, all three models maintain good accuracy even under the worst situations. Moreover, the error patterns and trends concerning different sensor stations and different hours on weekdays and weekends are also visualized and analyzed. Finally, based on these results, we separate the data into weekdays and weekends, train and test the models respectively, and improve the accuracy of the imputation result significantly

Imputation of Missing Traffic Flow Data by Using Denoising Autoencoders

In transportation engineering, Spatio-temporal data including traffic flow, speed, and occupancy are collected from different kinds of sensors and used by transportation engineers for analysis. However, the missing data influence the analysis and prediction results significantly. In this thesis, Denoising Autoencoders are used to impute the missing traffic flow data. First, we focused on the general situation and used three kinds of Denoising Autoencoders: “Vanilla”, CNN, and Bi-LSTM to implement the data with a general missing rate of 30%. Each model was optimized by focusing on the main hyper-parameters since the tuning can influence the accuracy of the final prediction result. Then, the Autoencoder models are used to train and test data with an exceptionally high missing rate of about 80%. We do this to test and then demonstrate that even under extreme loss conditions, Autoencoder models are very robust. By observing the hyper-parameter tuning process, the changing prediction accuracy is shown and in most cases, all three models maintain good accuracy even under the worst situations. Moreover, the error patterns and trends concerning different sensor stations and different hours on weekdays and weekends are also visualized and analyzed. Finally, based on these results, we separate the data into weekdays and weekends, train and test the models respectively, and improve the accuracy of the imputation result significantly