{"url":"/dataset/mainak-chakraborty","name":"Radar Dataset (DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition)","full_name":null,"description_markdown":"Abstract \r\n\r\nIn 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°).","description_withheld":null,"homepage":"https://ieee-dataport.org/documents/diat-%CE%BCradhar-radar-micro-doppler-signature-dataset-human-suspicious-activity-recognition","introduced_date":"2022-02-16","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Human Activity Recognition","url":"/task/human-activity-recognition","datasets_with_task":"/datasets/task/human-activity-recognition"}],"languages":[],"variants":["Radar Dataset (DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition)"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/classification-on-diat-mradhar-radar-micro","task":"Classification","dataset_variant":"Radar Dataset (DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition)","rows":1,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"μ RadNet","paper":"/paper/diat-m-radhar-micro-doppler-signature-dataset","metrics":{"1:1 Accuracy":"99.22"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/human-activity-recognition-on-diat-mradhar","task":"Human Activity Recognition","dataset_variant":"Radar Dataset (DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition)","rows":1,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"DIAT-RadHARNet","paper":"/paper/diat-radharnet-a-lightweight-dcnn-for-radar","metrics":{"1:1 Accuracy":"99.22"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/diat-radharnet-a-lightweight-dcnn-for-radar","title":"DIAT-RadHARNet: A lightweight DCNN for radar based classification of human suspicious activities","date":"2022-02-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/diat-m-radhar-micro-doppler-signature-dataset","title":"DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious activity recognition","date":"2022-02-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}