Person Property Estimation Based on 2D LiDAR Data Using Deep Neural Network

Video-based estimation plays a very significant role in person identification and tracking. The emergence of new technology and increased computational capabilities make the system robust and accurate day by day. Different RGB and dense cameras are used in these applications over time. Video-based analyses are offensive, and individual identity is leaked. As an alternative to visual capturing, now LiDAR shows its credentials with utmost accuracy. Besides privacy issues but critical natural circumstances also can be addressed with LiDAR sensing. Some susceptible scenarios like heavy fog and smoke in the environment are downward performed with typical visual estimation. In this study, we figured out a way of estimating a person's property, i.e., height and age, etc., based on LiDAR data. We placed different 2D LiDARs in ankle levels and captured persons' movements. These distance data are being processed as motion history images. We used deep neural architecture for estimating the properties of a person and achieved significant accuracies. This 2D LiDAR-based estimation can be a new pathway for critical reasoning and circumstances. Furthermore, computational cost and accuracies are very influential over traditional approaches.