Computational Imaging & VisAnalysis

#358: K. Nouduri, K. Gao, J. Fraser, S. Yao, H. AliAkbarpour, F. Bunyak, and K. Palaniappan

Deep realistic novel view generation for city-scale aerial images

IEEE International Conference on Pattern Recognition (ICPR), pgs. 7, 2021

3d voxel rendering, denoising, convolutional neural networks, synthetic aerial imagery, point cloud evaluation

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Rohit2021:ICPR_deep-realistic-views,
  author = "K. Nouduri and K. Gao and J. Fraser and S. Yao and H. AliAkbarpour and F. Bunyak and K. Palaniappan",
  title = "Deep realistic novel view generation for city-scale aerial images",
  year = 2021,
  journal = "IEEE International Conference on Pattern Recognition (ICPR)",
  pages = "7",
  month = "Jan",
  keywords = "3d voxel rendering, denoising, convolutional neural networks, synthetic aerial imagery, point cloud evaluation",
  doi = "10.1109/ICPR48806.2021.9412844"
}

K. Nouduri, K. Gao, J. Fraser, S. Yao, H. AliAkbarpour, F. Bunyak, and K. Palaniappan. Deep realistic novel view generation for city-scale aerial images. IEEE International Conference on Pattern Recognition (ICPR), pages 7, January 2021.

#349: C. Davis, J. Collins, J. Fraser, H. Zhang, S. Yao, E. Lattanzio, B. Balakrishnan, Y. Duan, P. Calyam, and K. Palaniappan

3D Modeling of Cities for Virtual Environments

2021 IEEE International Conference on Big Data (Big Data), pgs. 5587--5596, 2021

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{davis20213d,
  author = "C. Davis and J. Collins and J. Fraser and H. Zhang and S. Yao and E. Lattanzio and B. Balakrishnan and Y. Duan and P. Calyam and K. Palaniappan",
  title = "3D Modeling of Cities for Virtual Environments",
  year = 2021,
  booktitle = "2021 IEEE International Conference on Big Data (Big Data)",
  pages = "5587--5596"
}

C. Davis, J. Collins, J. Fraser, H. Zhang, S. Yao, E. Lattanzio, B. Balakrishnan, Y. Duan, P. Calyam, and K. Palaniappan. 3D Modeling of Cities for Virtual Environments. 2021 IEEE International Conference on Big Data (Big Data), pages 5587--5596, 2021.

#338: S. Yao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage

International Conference on Pattern Recognition, pgs. 32--45, 2021

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{yao2021-city-scale-stitching,
  author = "S. Yao and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = "City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage",
  year = 2021,
  booktitle = "International Conference on Pattern Recognition",
  pages = "32--45"
}

S. Yao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan. City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage. International Conference on Pattern Recognition, pages 32--45, 2021.

#320: K. Nouduri, F. Bunyak, S. Yao, H. Aliakbarpour, S. Agarwal, R. Rao, and K. Palaniappan

Deep Learning Based Landmark Matching For Aerial Geolocalization

2020 IEEE International Conference on Image Processing (ICIP), pgs. 1911-1915, 2020

Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Nouduri2020deep,
  author = "K. Nouduri and F. Bunyak and S. Yao and H. Aliakbarpour and S. Agarwal and R. Rao and K. Palaniappan",
  title = "Deep Learning Based Landmark Matching For Aerial Geolocalization",
  year = 2020,
  booktitle = "2020 IEEE International Conference on Image Processing (ICIP)",
  pages = "1911-1915",
  doi = "10.1109/ICIP40778.2020.9191342"
}

#310: S. Yao, Y. Wang, H. AliAkbarpour, G. Seetharaman, R. Rao, and K. Palaniappan

SMVNet: Deep Learning Architectures for Accurate and Robust Multi-View Stereopsis

2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1--6, 2020

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{yao2020smvnet,
  author = "S. Yao and Y. Wang and H. AliAkbarpour and G. Seetharaman and R. Rao and K. Palaniappan",
  title = "SMVNet: Deep Learning Architectures for Accurate and Robust Multi-View Stereopsis",
  year = 2020,
  booktitle = "2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1--6"
}

S. Yao, Y. Wang, H. AliAkbarpour, G. Seetharaman, R. Rao, and K. Palaniappan. SMVNet: Deep Learning Architectures for Accurate and Robust Multi-View Stereopsis. 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1--6, 2020.

#302: K. Gao, S. Yao, H. AliAkbarpour, S. Agarwal, G. Seetharaman, and K. Palaniappan

Sensitivity of multiview 3D point cloud reconstruction to compression quality and image feature detectability

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-6, 2019

jpeg compression, feature detection, 3d reconstruction, evaluation

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{KeGao2019:AIPR_Sensitivity-3d-compression,
  author = "K. Gao and S. Yao and H. AliAkbarpour and S. Agarwal and G. Seetharaman and K. Palaniappan",
  title = "Sensitivity of multiview 3D point cloud reconstruction to compression quality and image feature detectability",
  year = 2019,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-6",
  month = "Oct",
  keywords = "jpeg compression, feature detection, 3d reconstruction, evaluation",
  doi = "10.1109/AIPR47015.2019.9174580",
  url = "https://ieeexplore.ieee.org/document/9174580"
}

K. Gao, S. Yao, H. AliAkbarpour, S. Agarwal, G. Seetharaman, and K. Palaniappan. Sensitivity of multiview 3D point cloud reconstruction to compression quality and image feature detectability. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-6, October 2019.

#288: H. Trinh, P. Calyam, D. Chemodanov, S. Yao, Q. Lei, F. Gao, and K. Palaniappan

Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing

IEEE Transactions on Multimedia, Volume 20, pgs. 2562 - 2577, 2018

mobile edge computing, policy-based cloud management, energy-aware edge routing, low-latency visual data processing

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

New paradigms such as Mobile Edge Computing (MEC) are becoming feasible for use in, e.g., real-time decision-making during disaster incident response to handle the data deluge occurring in the network edge. However, MEC deployments today lack flexible IoT device data handling such as handling user preferences for real-time versus energy-efficient processing. Moreover, MEC can also benefit from a policy-based edge routing to handle sustained performance levels with efficient energy consumption. In this paper, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a facial recognition application that is important in disaster incident response scenarios, we propose a novel “offload decision-making” algorithm that analyzes the tradeoffs in computing policies to offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads. This algorithm also analyzes the impact on energy consumption in the decision-making under different visual data consumption requirements (i.e., users with thick clients or thin clients). To address the processing-throughput versus energy-efficiency tradeoffs, we propose a “Sustainable Policy-based Intelligence-Driven Edge Routing” algorithm that uses machine learning within Mobile Ad hoc Networks. This algorithm is energy aware and improves the geographic routing baseline performance (i.e., minimizes impact of local minima) for throughput performance sustainability, while also enabling flexible policy specification. We evaluate our proposed algorithms by conducting experiments on a realistic edge and core cloud testbed in the GENI Cloud infrastructure, and recreate disaster scenes of tornado damages within simulations. Our empirical results show how MEC can provide flexibility to users who desire energy conservation over low latency or vice versa in the visual data processing with a facial recognition application. In addition, our simulation results show that our routing approach outperforms existing solutions under diverse user preferences, node mobility, and severe node failure conditions.

@article{2018a,
  author = "H. Trinh and P. Calyam and D. Chemodanov and S. Yao and Q. Lei and F. Gao and K. Palaniappan",
  title = "Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing",
  year = 2018,
  journal = "IEEE Transactions on Multimedia",
  publisher = "IEEE",
  volume = 20,
  number = 10,
  pages = "2562 - 2577",
  month = "Aug",
  keywords = "mobile edge computing, policy-based cloud management, energy-aware edge routing, low-latency visual data processing",
  doi = " 10.1109/TMM.2018.2865661",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Energy-Aware+Mobile+Edge+Computing+and+Routing+for+Low-Latency+Visual+Data+Processing&btnG="
}

H. Trinh, P. Calyam, D. Chemodanov, S. Yao, Q. Lei, F. Gao, and K. Palaniappan. Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing. IEEE Transactions on Multimedia, IEEE, volume 20, issue 10, pages 2562 - 2577, August 2018.

#287: S. Yao, H. Yu, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

EpiX: A 3D Measurement Tool for Heritage, Archeology and Aerial Photogrammetry

Springer Book Chapter, 2018

cultural heritage, wide-area motion imagery, annotation tool, epipolar geometry

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{2018a,
  author = "S. Yao and H. Yu and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = "EpiX: A 3D Measurement Tool for Heritage, Archeology and Aerial Photogrammetry",
  year = 2018,
  journal = "Springer Book Chapter",
  month = "Jun",
  keywords = "cultural heritage, wide-area motion imagery, annotation tool, epipolar geometry"
}

S. Yao, H. Yu, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan. EpiX: A 3D Measurement Tool for Heritage, Archeology and Aerial Photogrammetry. Springer Book Chapter, June 2018.

#292: S. Yao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

3D patch-based multi-view stereo for high-resolution imagery

Geospatial Informatics, Motion Imagery, and Network Analytics VIII, pgs. 1-9, 2018

3d patch-based mvs, wami, 3d reconstruction, photometric consistency

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{2018a,
  author = "S. Yao and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = "3D patch-based multi-view stereo for high-resolution imagery",
  year = 2018,
  journal = "Geospatial Informatics, Motion Imagery, and Network Analytics VIII",
  pages = "1-9",
  month = "Apr",
  keywords = "3d patch-based mvs, wami, 3d reconstruction, photometric consistency",
  doi = "10.1117/12.2309806",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=3D+patch-based+multi-view+stereo+for+high-resolution+imagery&btnG="
}

S. Yao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan. 3D patch-based multi-view stereo for high-resolution imagery. Geospatial Informatics, Motion Imagery, and Network Analytics VIII, pages 1-9, April 2018.

#260: H. Trinh, D. Chemodanov, S. Yao, Q. Lei, B. Zhang, F. Gao, P. Calyam, and K. Palaniappan

Energy-aware mobile edge computing for low-latency visual data processing

IEEE International Conference on Future Internet of Things and Cloud (FiCloud), 2017

iot-based applications, cloud computing, energy awareness, visual data processing, mobile edge cloud

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{2017a,
  author = "H. Trinh and D. Chemodanov and S. Yao and Q. Lei and B. Zhang and F. Gao and P. Calyam and K. Palaniappan",
  title = "Energy-aware mobile edge computing for low-latency visual data processing",
  year = 2017,
  journal = "IEEE International Conference on Future Internet of Things and Cloud (FiCloud)",
  month = "Jan",
  keywords = "iot-based applications, cloud computing, energy awareness, visual data processing, mobile edge cloud",
  url = "https://par.nsf.gov/servlets/purl/10048928"
}

H. Trinh, D. Chemodanov, S. Yao, Q. Lei, B. Zhang, F. Gao, P. Calyam, and K. Palaniappan. Energy-aware mobile edge computing for low-latency visual data processing. IEEE International Conference on Future Internet of Things and Cloud (FiCloud), January 2017.

#218: M. Kristan, A. Leonardis, J. Matas, M. Felsberg, R. Pflugfelder, L. Cehovin, T. Vojir, G. Häger, A. Lukezic, G. Fernandez, A. Gupta, A. Petrosino, A. Memarmoghadam, A. Garcia-Martin, A. S. Montero, A. Vedaldi, A. Robinson, A. J. Ma, A. Varfolomieiev, A. Alatan, A. Erdem, B. Ghanem, B. Liu, B. Han, B. Martinez, C.-M. Chang, C. Xu, C. Sun, D. Kim, D. Chen, D. Du, D. Mishra, D.-Y. Yeung, E. Gundogdu, E. Erdem, F. Khan, F. Porikli, F. Zhao, F. Bunyak, F. Battistone, G. Zhu, G. Roffo, G. R. K. S. Subrahmanyam, G. Bastos, G. Seetharaman, H. Medeiros, H. Li, H. Qi, H. Bischof, H. Possegger, H. Lu, H. Lee, H. Nam, H. J. Chang, I. Drummond, J. Valmadre, J.-c. Jeong, J.-i. Cho, J.-Y. Lee, J. Zhu, J. Feng, J. Gao, J. Y. Choi, J. Xiao, J.-W. Kim, J. Jeong, J. F. Henriques, J. Lang, J. Choi, J. M. Martinez, J. Xing, J. Gao, K. Palaniappan, K. Lebeda, K. Gao, K. Mikolajczyk, L. Qin, L. Wang, L. Wen, L. Bertinetto, M. K. Rapuru, M. Poostchi, M. Maresca, M. Danelljan, M. Mueller, M. Zhang, M. Arens, M. Valstar, M. Tang, M. Baek, M. H. Khan, N. Wang, N. Fan, N. Al-Shakarji, O. Miksik, O. Akin, P. Moallem, P. Senna, P. H. S. Torr, P. C. Yuen, Q. Huang, R. Martin-Nieto, R. Pelapur, R. Bowden, R. Laganière, R. Stolkin, R. Walsh, S. B. Krah, S. Li, S. Zhang, S. Yao, S. Hadfield, S. Melzi, S. Lyu, S. Li, S. Becker, S. Golodetz, S. Kakanuru, S. Choi, T. Hu, T. Mauthner, T. Zhang, T. Pridmore, V. Santopietro, W. Hu, W. Li, W. Hübner, X. Lan, X. Wang, X. Li, Y. Li, Y. Demiris, Y. Wang, Y. Qi, Z. Yuan, Z. Cai, Z. Xu, Z. He, and Z. Chi

The visual object tracking VOT2016 challenge results

European Conference on Computer Vision Workshop (ECCVW), pgs. 777--823, 2016

performance, evaluation, short-term, single-object trackers, vot

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Noor:ECCV_VOT_report,
author = "M. Kristan and A. Leonardis and J. Matas and M. Felsberg and R. Pflugfelder and L. Cehovin and T. Vojir and G. Häger and A. Lukezic and G. Fernandez and A. Gupta and A. Petrosino and A. Memarmoghadam and A. Garcia-Martin and A. S. Montero and A. Vedaldi and A. Robinson and A. J. Ma and A. Varfolomieiev and A. Alatan and A. Erdem and B. Ghanem and B. Liu and B. Han and B. Martinez and C.-M. Chang and C. Xu and C. Sun and D. Kim and D. Chen and D. Du and D. Mishra and D.-Y. Yeung and E. Gundogdu and E. Erdem and F. Khan and F. Porikli and F. Zhao and F. Bunyak and F. Battistone and G. Zhu and G. Roffo and G. R. K. S. Subrahmanyam and G. Bastos and G. Seetharaman and H. Medeiros and H. Li and H. Qi and H. Bischof and H. Possegger and H. Lu and H. Lee and H. Nam and H. J. Chang and I. Drummond and J. Valmadre and J.-c. Jeong and J.-i. Cho and J.-Y. Lee and J. Zhu and J. Feng and J. Gao and J. Y. Choi and J. Xiao and J.-W. Kim and J. Jeong and J. F. Henriques and J. Lang and J. Choi and J. M. Martinez and J. Xing and J. Gao and K. Palaniappan and K. Lebeda and K. Gao and K. Mikolajczyk and L. Qin and L. Wang and L. Wen and L. Bertinetto and M. K. Rapuru and M. Poostchi and M. Maresca and M. Danelljan and M. Mueller and M. Zhang and M. Arens and M. Valstar and M. Tang and M. Baek and M. H. Khan and N. Wang and N. Fan and N. Al-Shakarji and O. Miksik and O. Akin and P. Moallem and P. Senna and P. H. S. Torr and P. C. Yuen and Q. Huang and R. Martin-Nieto and R. Pelapur and R. Bowden and R. Laganière and R. Stolkin and R. Walsh and S. B. Krah and S. Li and S. Zhang and S. Yao and S. Hadfield and S. Melzi and S. Lyu and S. Li and S. Becker and S. Golodetz and S. Kakanuru and S. Choi and T. Hu and T. Mauthner and T. Zhang and T. Pridmore and V. Santopietro and W. Hu and W. Li and W. Hübner and X. Lan and X. Wang and X. Li and Y. Li and Y. Demiris and Y. Wang and Y. Qi and Z. Yuan and Z. Cai and Z. Xu and Z. He and Z. Chi",
title = "The visual object tracking VOT2016 challenge results",
year = 2016,
booktitle = "European Conference on Computer Vision Workshop (ECCVW)",
pages = "777--823",
keywords = "performance, evaluation, short-term, single-object trackers, vot",
url = "https://link.springer.com/chapter/10.1007/978-3-319-48881-3_54"
}

M. Kristan, A. Leonardis, J. Matas, M. Felsberg, R. Pflugfelder, L. Cehovin, T. Vojir, G. Häger, A. Lukezic, G. Fernandez, A. Gupta, A. Petrosino, A. Memarmoghadam, A. Garcia-Martin, A. S. Montero, A. Vedaldi, A. Robinson, A. J. Ma, A. Varfolomieiev, A. Alatan, A. Erdem, B. Ghanem, B. Liu, B. Han, B. Martinez, C.-M. Chang, C. Xu, C. Sun, D. Kim, D. Chen, D. Du, D. Mishra, D.-Y. Yeung, E. Gundogdu, E. Erdem, F. Khan, F. Porikli, F. Zhao, F. Bunyak, F. Battistone, G. Zhu, G. Roffo, G. R. K. S. Subrahmanyam, G. Bastos, G. Seetharaman, H. Medeiros, H. Li, H. Qi, H. Bischof, H. Possegger, H. Lu, H. Lee, H. Nam, H. J. Chang, I. Drummond, J. Valmadre, J.-c. Jeong, J.-i. Cho, J.-Y. Lee, J. Zhu, J. Feng, J. Gao, J. Y. Choi, J. Xiao, J.-W. Kim, J. Jeong, J. F. Henriques, J. Lang, J. Choi, J. M. Martinez, J. Xing, J. Gao, K. Palaniappan, K. Lebeda, K. Gao, K. Mikolajczyk, L. Qin, L. Wang, L. Wen, L. Bertinetto, M. K. Rapuru, M. Poostchi, M. Maresca, M. Danelljan, M. Mueller, M. Zhang, M. Arens, M. Valstar, M. Tang, M. Baek, M. H. Khan, N. Wang, N. Fan, N. Al-Shakarji, O. Miksik, O. Akin, P. Moallem, P. Senna, P. H. S. Torr, P. C. Yuen, Q. Huang, R. Martin-Nieto, R. Pelapur, R. Bowden, R. Laganière, R. Stolkin, R. Walsh, S. B. Krah, S. Li, S. Zhang, S. Yao, S. Hadfield, S. Melzi, S. Lyu, S. Li, S. Becker, S. Golodetz, S. Kakanuru, S. Choi, T. Hu, T. Mauthner, T. Zhang, T. Pridmore, V. Santopietro, W. Hu, W. Li, W. Hübner, X. Lan, X. Wang, X. Li, Y. Li, Y. Demiris, Y. Wang, Y. Qi, Z. Yuan, Z. Cai, Z. Xu, Z. He, and Z. Chi. The visual object tracking VOT2016 challenge results. European Conference on Computer Vision Workshop (ECCVW), pages 777--823, 2016.

#217: M. Felsberg, M. Kristan, J. Matas, A. Leonardis, R. Pflugfelder, G. Häger, A. Berg, A. Eldesokey, J. Ahlberg, L. Cehovin, T. Vojir, A. Lukezic, G. Fernandez, A. Petrosino, A. Garcia-Martin, A. S. Montero, A. Varfolomieiev, A. Erdem, B. Han, C.-M. Chang, D. Du, E. Erdem, F. S. Khan, F. Porikli, F. Zhao, F. Bunyak, F. Battistone, G. Zhu, G. Seetharaman, H. Li, H. Qi, H. Bischof, H. Possegger, H. Nam, J. Valmadre, J. Zhu, J. Feng, J. Lang, J. M. Martinez, K. Palaniappan, K. Lebeda, K. Gao, K. Mikolajczyk, L. Wen, L. Bertinetto, M. Poostchi, M. Maresca, M. Danelljan, M. Arens, M. Tang, M. Baek, N. Fan, N. Al-Shakarji, O. Miksik, O. Akin, P. H. S. Torr, Q. Huang, R. Martin-Nieto, R. Pelapur, R. Bowden, R. Laganière, S. B. Krah, S. Li, S. Yao, S. Hadfield, S. Lyu, S. Becker, S. Golodetz, T. Hu, T. Mauthner, V. Santopietro, W. Li, W. Hübner, X. Li, Y. Li, Z. Xu, and Z. He

The thermal infrared visual object tracking VOT-TIR2016 challenge results

European Conference on Computer Vision Workshop (ECCVW), pgs. 824--849, 2016

performance, evaluation, object tracking, thermal, ir, vot

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Noor2016:ECCV_Thermal_VOT_report,
author = "M. Felsberg and M. Kristan and J. Matas and A. Leonardis and R. Pflugfelder and G. Häger and A. Berg and A. Eldesokey and J. Ahlberg and L. Cehovin and T. Vojir and A. Lukezic and G. Fernandez and A. Petrosino and A. Garcia-Martin and A. S. Montero and A. Varfolomieiev and A. Erdem and B. Han and C.-M. Chang and D. Du and E. Erdem and F. S. Khan and F. Porikli and F. Zhao and F. Bunyak and F. Battistone and G. Zhu and G. Seetharaman and H. Li and H. Qi and H. Bischof and H. Possegger and H. Nam and J. Valmadre and J. Zhu and J. Feng and J. Lang and J. M. Martinez and K. Palaniappan and K. Lebeda and K. Gao and K. Mikolajczyk and L. Wen and L. Bertinetto and M. Poostchi and M. Maresca and M. Danelljan and M. Arens and M. Tang and M. Baek and N. Fan and N. Al-Shakarji and O. Miksik and O. Akin and P. H. S. Torr and Q. Huang and R. Martin-Nieto and R. Pelapur and R. Bowden and R. Laganière and S. B. Krah and S. Li and S. Yao and S. Hadfield and S. Lyu and S. Becker and S. Golodetz and T. Hu and T. Mauthner and V. Santopietro and W. Li and W. Hübner and X. Li and Y. Li and Z. Xu and Z. He",
title = "The thermal infrared visual object tracking VOT-TIR2016 challenge results",
year = 2016,
booktitle = "European Conference on Computer Vision Workshop (ECCVW)",
pages = "824--849",
keywords = "performance, evaluation, object tracking, thermal, ir, vot",
url = "https://link.springer.com/chapter/10.1007/978-3-319-48881-3_55"
}

M. Felsberg, M. Kristan, J. Matas, A. Leonardis, R. Pflugfelder, G. Häger, A. Berg, A. Eldesokey, J. Ahlberg, L. Cehovin, T. Vojir, A. Lukezic, G. Fernandez, A. Petrosino, A. Garcia-Martin, A. S. Montero, A. Varfolomieiev, A. Erdem, B. Han, C.-M. Chang, D. Du, E. Erdem, F. S. Khan, F. Porikli, F. Zhao, F. Bunyak, F. Battistone, G. Zhu, G. Seetharaman, H. Li, H. Qi, H. Bischof, H. Possegger, H. Nam, J. Valmadre, J. Zhu, J. Feng, J. Lang, J. M. Martinez, K. Palaniappan, K. Lebeda, K. Gao, K. Mikolajczyk, L. Wen, L. Bertinetto, M. Poostchi, M. Maresca, M. Danelljan, M. Arens, M. Tang, M. Baek, N. Fan, N. Al-Shakarji, O. Miksik, O. Akin, P. H. S. Torr, Q. Huang, R. Martin-Nieto, R. Pelapur, R. Bowden, R. Laganière, S. B. Krah, S. Li, S. Yao, S. Hadfield, S. Lyu, S. Becker, S. Golodetz, T. Hu, T. Mauthner, V. Santopietro, W. Li, W. Hübner, X. Li, Y. Li, Z. Xu, and Z. He. The thermal infrared visual object tracking VOT-TIR2016 challenge results. European Conference on Computer Vision Workshop (ECCVW), pages 824--849, 2016.

#193: M. Kristan, J. Matas, A. Leonardis, M. Felsberg, .., R. Pelapur, K. Palaniappan, F. Bunyak, M. Poostchi, S. Yao, and K. Gao

The Visual Object Tracking VOT2015 challenge results

IEEE International Conference on Computer Vision Workshops (ICCVW), pgs. 564-586, 2015

wami, tracking, fmv, features, motion, image analysis

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Kristan2015:ICCVW_VOT2015,
  author = "M. Kristan and J. Matas and A. Leonardis and M. Felsberg and .. and R. Pelapur and K. Palaniappan and F. Bunyak and M. Poostchi and S. Yao and K. Gao",
  title = "The Visual Object Tracking VOT2015 challenge results",
  year = 2015,
  journal = "IEEE International Conference on Computer Vision Workshops (ICCVW)",
  pages = "564-586",
  month = "Dec",
  keywords = "wami, tracking, fmv, features, motion, image analysis",
  doi = "10.1109/ICCVW.2015.79",
  url = "http://www.votchallenge.net/vot2015/program.html"
}

M. Kristan, J. Matas, A. Leonardis, M. Felsberg, .., R. Pelapur, K. Palaniappan, F. Bunyak, M. Poostchi, S. Yao, and K. Gao. The Visual Object Tracking VOT2015 challenge results. IEEE International Conference on Computer Vision Workshops (ICCVW), pages 564-586, December 2015.

13 Publications by "S. Yao"

3d voxel rendering, denoising, convolutional neural networks, synthetic aerial imagery, point cloud evaluation

jpeg compression, feature detection, 3d reconstruction, evaluation

mobile edge computing, policy-based cloud management, energy-aware edge routing, low-latency visual data processing

cultural heritage, wide-area motion imagery, annotation tool, epipolar geometry

3d patch-based mvs, wami, 3d reconstruction, photometric consistency

iot-based applications, cloud computing, energy awareness, visual data processing, mobile edge cloud

performance, evaluation, short-term, single-object trackers, vot

performance, evaluation, object tracking, thermal, ir, vot

wami, tracking, fmv, features, motion, image analysis