{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/diat-radharnet-a-lightweight-dcnn-for-radar","title":"DIAT-RadHARNet: A lightweight DCNN for radar based classification of human suspicious activities","arxiv_id":null,"date":"2022-02-01","proceeding":"IEEE Transactions on Instrumentation and Measurement 2022 2","authors":["Mainak Chakraborty","Harish C. Kumawat","Sunita Vikrant Dhavale","Arockia Bazil Raj A"],"abstract":"Recognizing suspicious human activities is one of the critical requirements for national security considerations. Nowadays, designing the deep convolution neural network (DCNN) models suitable for micro-Doppler (m-D) signature-based human activity classification is rapidly growing. However, high computation cost and a huge number of parameters limit their direct/effective usability in field applications. This article introduces an m-D signatures’ dataset “DIAT- μ RadHAR” covering army crawling, boxing, jumping while holding a gun, army jogging, army marching, and stone-pelting/grenade-throwing, generated using an X -band continuous wave (CW) radar. This article also introduces a lightweight DCNN model, “DIAT-RadHARNet,” designed for those human suspicious activity classification. To reduce the computation cost and to improve the generalization ability, DIAT-RadHARNet is designed with four design principles: depthwise separable convolutions, channel weighting (CHW) based on the importance, different size filters in the depthwise part, and operating different size kernels on the same input tensor. The network has 213 793 parameters with a total of 55 layers. Our extensive experimental analysis demonstrates that the DIAT-RadHARNet model efficiently classifies the activities with 99.22% accuracy, giving minimal false positive and false negative outcomes. The time complexity of the proposed DCNN model observed during the testing phase is 0.35 s. The same accuracy and time complexity are obtained even at adverse weather conditions, low-lighting environments, and long-range operations.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9721839","url_pdf":"https://ieeexplore.ieee.org/abstract/document/9721839","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/human-activity-recognition-on-diat-mradhar","task":"Human Activity Recognition","dataset":"Radar Dataset (DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition)","model":"DIAT-RadHARNet","rank_in_archive_order":1,"of":1,"metrics":{"1:1 Accuracy":"99.22"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}