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DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition
- Citation Author(s):
-
MainakChakraborty
Defence Institute of Advanced Technology (DIAT), Girinagar, Pune 411025, IndiaHarish C.KumawatDefence Institute of Advanced Technology (DIAT), Girinagar, Pune 411025, IndiaSunita VikrantDhavaleDefence Institute of Advanced Technology (DIAT), Girinagar, Pune 411025, IndiaA. ArockiaBazil RajDefence Institute of Advanced Technology (DIAT), Girinagar, Pune 411025, India - Submitted by:
- Mainak Chakraborty
- Last updated:
- Thu, 10/27/2022 - 23:57
- DOI:
- 10.21227/015m-7415
- Data Format:
- Research Article Link:
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5522 ViewsAbstract
In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory. Designing an automated human suspicious activities: army crawling, army jogging, jumping with holding a gun, army marching, boxing, and stone-pelting/grenades-throwing, recognition system using a suitable deep convolutional neural network (DCNN) model is rapidly growing due to its inherent in-depth features extraction capability. As a value addition to this research, an X-band continuous wave (CW) 10 GHz radar has been developed at our radar systems laboratory and used to acquire the m-D signatures, to prepare a dataset (DIAT-μRadHAR) corresponding to above mentioned suspicious activities. In order to prepare a realistic dataset, human targets of different heights, weights, and gender are directed to perform the suspicious activities in front of the radar at different ranges between 10 m - 0.5 km and at different target aspect angles (0°, ±15°, ±30° and ±45°).
In our dataset, the total number of spectrogram images generated using the open-field experiments is 3780, and the class-wise details can be found in our journal articles (1) M. Chakraborty, H. C. Kumawat, S. V. Dhavale and A. A. B. Raj, "DIAT-μ RadHAR (Micro-Doppler Signature Dataset) & μ RadNet (A Lightweight DCNN)—For Human Suspicious Activity Recognition," in IEEE Sensors Journal, vol. 22, no. 7, pp. 6851-6858, 1 April1, 2022, doi: 10.1109/JSEN.2022.3151943. (2) M. Chakraborty, H. C. Kumawat, S. V. Dhavale and A. B. Raj A., "DIAT-RadHARNet: A Lightweight DCNN for Radar Based Classification of Human Suspicious Activities," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10, 2022, Art no. 2505210, doi: 10.1109/TIM.2022.3154832. (3) M. Chakraborty, H. C. Kumawat, S. V. Dhavale and A. B. Raj A., "Application of DNN for radar micro-doppler signature-based human suspicious activity recognition." in Pattern Recognition Letters, vol. 162 , pp. 1-6, 2022, doi: https://doi.org/10.1016/j.patrec.2022.08.005.
The dataset consist of 3780 spectrogram images (Image JPG File (.JPG) and .MAT) corresponding to micro-Doppler signatures of different human activities; namely (a) army marching, (b) Stone pelting/Grenades throwing, (c) jumping with holding a gun, (d) army Jogging, (e) army crawling and (f) boxing activities.
The DIAT-μRadHAR dataset is completely open to academic research. To use the dataset, please cite the following base/original papers:
(1) M. Chakraborty, H. C. Kumawat, S. V. Dhavale and A. A. B. Raj, "DIAT-μ RadHAR (Micro-Doppler Signature Dataset) & μ RadNet (A Lightweight DCNN)—For Human Suspicious Activity Recognition," in IEEE Sensors Journal, vol. 22, no. 7, pp. 6851-6858, 1 April1, 2022, doi: 10.1109/JSEN.2022.3151943.
(2) M. Chakraborty, H. C. Kumawat, S. V. Dhavale and A. B. Raj A., "DIAT-RadHARNet: A Lightweight DCNN for Radar Based Classification of Human Suspicious Activities," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10, 2022, Art no. 2505210, doi: 10.1109/TIM.2022.3154832.
(3) M. Chakraborty, H. C. Kumawat, S. V. Dhavale and A. B. Raj A., "Application of DNN for radar micro-doppler signature-based human suspicious activity recognition." in Pattern Recognition Letters, vol. 162 , pp. 1-6, 2022, doi: https://doi.org/10.1016/j.patrec.2022.08.005.
Dataset Files
- Image JPG File (.JPG) corresponding to micro-Doppler signatures of different human activities; namely (a) army marchin DIAT-RadHAR.zip (166.78 MB)
- .mat files corresponding to micro-Doppler signatures of different human activities; namely (a) a HAR mat files.rar (848.90 MB)
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Suspicious human activity recognition dataasets