Update README.md

README.md

@@ -282,15 +282,15 @@ OmniFall addresses three critical limitations in current fall detection research
 
 This benchmark includes annotations for the following datasets:
 
-1. **CMDFall** (7h 7m) - Multi-subject, multi-view dataset
-2. **UP Fall** (4h 35m) - Multiple subjects with two camera views
-3. **Le2i** (47m) - Multiple room settings
-4. **GMDCSA24** (21m) - Multiple room settings
-5. **CAUCAFall** (16m)
-6. **OCCU** (14m) - Two camera views
-7. **EDF** (13m) - Two camera views
-8. **MCFD** (12m) - Single subject, eight camera views
-9. **OOPS-Fall** - Curated subset of genuine fall accidents from the OOPS dataset
+1. **[CMDFall](https://www.mica.edu.vn/perso/Tran-Thi-Thanh-Hai/CMDFALL.html)** (7h 25m single view) - 50 subjects, 7 synchronized views
+2. **[UP Fall](https://sites.google.com/up.edu.mx/har-up/)** (4h 35m) - 17 subjects, 2 synchronized views
+3. **[Le2i](https://search-data.ubfc.fr/imvia/FR-13002091000019-2024-04-09_Fall-Detection-Dataset.html)** (47m) - 9 subjects, 6 different rooms
+4. **[GMDCSA24](https://github.com/ekramalam/GMDCSA24-A-Dataset-for-Human-Fall-Detection-in-Videos)** (21m) - 4 subjects, 3 rooms
+5. **[CAUCAFall](https://data.mendeley.com/datasets/7w7fccy7ky/4)** (16m) - 10 subjects, 1 room
+6. **[EDF](https://doi.org/10.5281/zenodo.15494102)** (13m) - 5 subjects, 2 views synchronized
+7. **[OCCU](https://doi.org/10.5281/zenodo.15494102)** (14m) - 5 subjects, 2 views not synchronized
+8. **[MCFD](https://www.iro.umontreal.ca/~labimage/Dataset/)** (12m) - 1 subject, 8 views
+9. **[OOPS-Fall](https://oops.cs.columbia.edu/data/)** - Curated subset of genuine fall accidents from the OOPS dataset, strong variation in subjects and views.
 
 ## Structure
 
@@ -421,14 +421,89 @@ for split_name, split_data in [("CV", cv_split), ("CS", cs_split)]:
 
 ## Citation
 
-If you use OmniFall in your research, please cite our paper (will be updated soon):
+If you use OmniFall in your research, please cite our paper (will be updated soon) as well as all sub-dataset papers:
 
 ```bibtex
-@inproceedings{omnifall2025,
+@inproceedings{omnifall,
   title={OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection},
   author={},
   booktitle={},
   year={2025},
+},
+
+@inproceedings{omnifall_cmdfall,
+  title={A multi-modal multi-view dataset for human fall analysis and preliminary investigation on modality},
+  author={Tran, Thanh-Hai and Le, Thi-Lan and Pham, Dinh-Tan and Hoang, Van-Nam and Khong, Van-Minh and Tran, Quoc-Toan and Nguyen, Thai-Son and Pham, Cuong},
+  booktitle={2018 24th International Conference on Pattern Recognition (ICPR)},
+  pages={1947--1952},
+  year={2018},
+  organization={IEEE}
+},
+
+@article{omnifall_up-fall,
+  title={UP-fall detection dataset: A multimodal approach},
+  author={Mart{\'\i}nez-Villase{\~n}or, Lourdes and Ponce, Hiram and Brieva, Jorge and Moya-Albor, Ernesto and N{\'u}{\~n}ez-Mart{\'\i}nez, Jos{\'e} and Pe{\~n}afort-Asturiano, Carlos},
+  journal={Sensors},
+  volume={19},
+  number={9},
+  pages={1988},
+  year={2019},
+  publisher={MDPI}
+},
+
+@article{omnifall_le2i,
+  title={Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification},
+  author={Charfi, Imen and Miteran, Johel and Dubois, Julien and Atri, Mohamed and Tourki, Rached},
+  journal={Journal of Electronic Imaging},
+  volume={22},
+  number={4},
+  pages={041106--041106},
+  year={2013},
+  publisher={Society of Photo-Optical Instrumentation Engineers}
+},
+
+@article{omnifall_gmdcsa,
+  title={GMDCSA-24: A dataset for human fall detection in videos},
+  author={Alam, Ekram and Sufian, Abu and Dutta, Paramartha and Leo, Marco and Hameed, Ibrahim A},
+  journal={Data in Brief},
+  volume={57},
+  pages={110892},
+  year={2024},
+  publisher={Elsevier}
+},
+
+@article{omnifall_cauca,
+  title={Dataset CAUCAFall},
+  author={Eraso, Jose Camilo and Mu{\~n}oz, Elena and Mu{\~n}oz, Mariela and Pinto, Jesus},
+  journal={Mendeley Data},
+  volume={4},
+  year={2022}
+},
+
+@inproceedings{omnifall_edf_occu,
+  title={Evaluating depth-based computer vision methods for fall detection under occlusions},
+  author={Zhang, Zhong and Conly, Christopher and Athitsos, Vassilis},
+  booktitle={International symposium on visual computing},
+  pages={196--207},
+  year={2014},
+  organization={Springer}
+},
+
+@article{omnifall_mcfd,
+  title={Multiple cameras fall dataset},
+  author={Auvinet, Edouard and Rougier, Caroline and Meunier, Jean and St-Arnaud, Alain and Rousseau, Jacqueline},
+  journal={DIRO-Universit{\'e} de Montr{\'e}al, Tech. Rep},
+  volume={1350},
+  pages={24},
+  year={2010}
+},
+
+@inproceedings{omnifall_oops,
+  title={Oops! predicting unintentional action in video},
+  author={Epstein, Dave and Chen, Boyuan and Vondrick, Carl},
+  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
+  pages={919--929},
+  year={2020}
 }
 ```