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:v: Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset
:v: Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset
1Modelling and Simulation Centre, Viettel High Technology Industries Corporation, Vietnam
2School of Electronics and Telecommunications, Hanoi University of Science and Technology, Vietnam
3International Research Institute MICA, Hanoi University of Science and Technology, Vietnam
✉Corresponding Author
Egocentric vision is an emerging field of computer vision characterized by the acquisition of video from the first-person perspective. Particularly, for evaluating upper extremity rehabilitation, egocentric vision offers the ability to quantitatively measure the function of hands used in physical-based exercises. For such applications, hand detection and tracking are the first requirements. In this work, we develop a fully automatic tracking-by-detection pipeline that firstly extracts hand positions and then tracks hands in consecutive frames. The proposed framework consists of state-of-the-art detectors such as RCNN and YOLO family models coupled with advanced trackers (e.g., SORT and DeepSORT) for the tracking task. This paper explores how the performance of the stand-alone object detection algorithms correlates with overall performance of the tracking-by-detection system. The experimental results show that detection highly impacts the overall performance. Moreover, this work also proves that the use of visual descriptors in the tracking stage can reduce the number of identity switches and thereby increase the potential of the whole system. We also present challenges for new egocentric hand-tracking dataset for future works.
:clap: News
- [2021.08.21] Best runner-up presentation award at RIVF 2021.
- [2021.04.15] MICARehab dataset released as a benchmark for hand detection and tracking from FPV.
- [2021.04.10] Paper is accepted to RIVF 2021.
- [2020.10.31] Related master thesis is successfully defended at SOICT, HUST.
- [2020.06.04] Demo code and pre-trained model released.
:ok_hand: Main results
Object detection and segmentation AP and AR following the COCO standard.
| Algorithm | AP | AP50 | AP75 | APsmall | APmedium | APlarge | ARmax=1 | ARmax=10 | ARmax=100 | ARsmall | ARmedium | ARlarge |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Yolov3 | 89.2 | 92.4 | 92.1 | 1.1 | 66.4 | 54.1 | 6.5 | 53.6 | 76.4 | 3.2 | 32.5 | 75.9 |
| Yolov4x | 93.1 | 95.6 | 94.6 | 3.2 | 72.5 | 42.9 | 8.7 | 65.8 | 89.7 | 7.1 | 40.1 | 82.7 |
| FasterRCNN | 96.2 | 97.9 | 97.9 | 0.9 | 75.8 | 6.3 | 9.6 | 76.8 | 97.6 | 10.0 | 77.8 | 97.6 |
| MaskRCNN | 92.1 | 98.9 | 97.9 | 0.0 | 32.4 | 92.2 | 9.2 | 73.9 | 94.6 | 0.0 | 50.8 | 94.7 |
Tracking result on MICARehab following MOT16 evaluation protocol.
| Method | IDF1 | IDP | IDR | Rcll | Prcn | GT | MT | PT | ML | FP | FN | IDs | FM | MOTA | MOTP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Y3S | 51.4 | 59.4 | 45.2 | 75.3 | 99.4 | 24 | 7 | 8 | 9 | 68 | 3630 | 123 | 174 | 74.1 | 0.133 |
| Y4S | 56.7 | 60.7 | 53.0 | 86.4 | 99.4 | 24 | 9 | 11 | 4 | 81 | 1996 | 134 | 159 | 85.0 | 0.127 |
| FS | 74.5 | 73.9 | 74.8 | 97.9 | 97.1 | 24 | 17 | 7 | 0 | 426 | 306 | 115 | 91 | 94.2 | 0.082 |
| MS | 74.5 | 73.9 | 74.8 | 97.9 | 97.2 | 24 | 17 | 7 | 0 | 420 | 304 | 114 | 90 | 94.3 | 0.082 |
| GS | 89.1 | 89.3 | 88.7 | 98.5 | 99.6 | 24 | 21 | 3 | 0 | 62 | 220 | 91 | 50 | 97.5 | 0.059 |
| Y3DS | 58.7 | 66.0 | 52.6 | 78.4 | 98.7 | 24 | 9 | 7 | 8 | 149 | 3176 | 123 | 202 | 76.6 | 0.151 |
| Y4DS | 65.0 | 68.1 | 61.9 | 89.3 | 98.5 | 24 | 11 | 9 | 4 | 194 | 1581 | 122 | 192 | 87.1 | 0.142 |
| FDS | 79.4 | 79.0 | 79.5 | 98.1 | 97.8 | 24 | 17 | 7 | 0 | 320 | 282 | 117 | 75 | 95.1 | 0.060 |
| MDS | 83.5 | 83.5 | 83.3 | 98.1 | 98.7 | 24 | 18 | 5 | 1 | 184 | 275 | 95 | 61 | 96.2 | 0.054 |
| GDS | 88.5 | 88.5 | 88.1 | 99.1 | 99.9 | 24 | 23 | 1 | 0 | 12 | 135 | 82 | 43 | 98.4 | 0.052 |
:point_right: Installation
Please refer to INSTALL.md for installation instructions.
:raised_hands: Model zoo
Trained models are available in the MODEL_ZOO.md.
:open_hands: Dataset zoo
Please see DATASET_ZOO.md for a detailed description of the training/evaluation datasets.
:point_down: Getting Started
Follow the aforementioned instructions to install D2DP and download models and datasets.
GETTING_STARTED.md provides a brief intro of the usage of built-in command-line tools in D2DP.
:+1: Supplementary materials
More details can be found here.
:call_me_hand: Citation
If you use this work in your research or wish to refer to the results, please use the following BibTeX entry.
@inproceedings{pham2021detection,
title={Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset},
author={Pham, Van-Tien and Tran, Thanh-Hai and Vu, Hai},
booktitle={2021 RIVF International Conference on Computing and Communication Technologies (RIVF)},
pages={1--6},
year={2021},
organization={IEEE}
}
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