The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor
Abstract
Human action recognition has a wide range of applications including biometrics, surveillance, and human computer interaction. The use of multimodal sensors for human action recognition is steadily increasing. However, there are limited publicly available datasets where depth camera and inertial sensor data are captured at the same time. This paper describes a freely available dataset, named UTD-MHAD, which consists of four temporally synchronized data modalities. These modalities include RGB videos, depth videos, skeleton positions, and inertial signals from a Kinect camera and a wearable inertial sensor for a comprehensive set of 27 human actions. Experimental results are provided to show how this database can be used to study fusion approaches that involve using both depth camera data and inertial sensor data. This public domain dataset is of benefit to multimodality research activities being conducted for human action recognition by various research groups.
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Cites
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
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Cited by
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Shahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.115 · Cited by 3,148
Deep multimodal learning: a survey on recent advances and trends
Ramachandram, D., & Taylor, G. W. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2738401 · Cited by 1,139
Sensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625
“While most datasets are used to recognize physical activity or daily activities [19, 20, 30, 31, 47, 59, 61, 143, 155, 188, 228, 251, 252, 264, 279, 282, 299, 327, 335], there are also a few datasets dedicated to hand gestures [49, 156], breathing patterns [58], and car assembly line activities [316], as well as those…”
BackgroundMethodA Comprehensive Survey of Vision-Based Human Action Recognition Methods
Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019) · Sensors · doi:10.3390/s19051005 · Cited by 503
“Hollywood2 [97] √ × × 1707 12 HMDB51 [98] √ × × 6766 51 Olympic Sports [99] √ × × 783 16 UCF50 [100] √ × × 6618 50 UCF101 [101] √ × × 13,320 101 Kinetics [102] √ × × 306,245 400 MSR-Action3D [103] × √ √ 567 20 MSR-Daily Activity [104] √ √ √ 320 16 Northwestern-UCLA [105] √ √ √ 1475 10 UTD-MHAD [106] √ √ √ 861 27 RGBD-…”
Metadata
- Authors
- Chen, C., Jafari, R., & Kehtarnavaz, N.
- Venue
- 2015 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2015-09
- Pages
- 168-172
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2296311849
- Cited by
- 765
- References
- 1
- FWCI
- 17.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, C., Jafari, R., & Kehtarnavaz, N. (2015). UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. 2015 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2015.7350781
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