Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

Jnana Sai Abhishek Varma Gokaraju, Weon Keun Song, Min Ho Ka, Somyot Kaitwanidvilai

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

Original languageEnglish
JournalInternational Journal of Advanced Robotic Systems
Issue number3
Publication statusPublished - 2021

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by King Mongkut’s Institute of Technology Ladkrabang (KMITL) of Thailand (grant number: KREF 186311); and the grant-in-aid of HANWHA SYSTEMS, South Korea.

Publisher Copyright:
© The Author(s) 2021.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Artificial Intelligence


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