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Video-based recognition techniques are solemnly effective, and it comes to a new era of research nowadays. Yet again, it suffers some bottlenecks indeed. Situations, surroundings, and momentums may be disgraceful with all new inventions. So, to solve the drawbacks of technology is to imply a new technology on it. Biometric features are very authentic and high valued measures for human identifications. Most of the techniques are dependent on close contact with the subject. A gait is a pattern that performs by walking from the individual. Almost all studies of gait-based person identifications are performed by RGB or RGB-D cameras. Very few studies were done by using LiDAR data. Applying 2D LiDAR images for individual tracking and identification is superb when video surveillances fail to perform accurately due to environmental and imposed difficulties (i.e., disaster, rain, fog, smoke, snow, occlusion, cost, etc.). This research performed a comprehensive exhibition of 2D LiDAR data with a rigorous self-made dataset and customized residual neural network. We considered different experimental setups and found exciting precisions there. Our system is appropriate for recognizing a person based on his ankle level 2D LiDAR data.

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Along with the progress of deep learning techniques, people tracking using video cameras became easy and accurate. However, privacy and security issues are not enough to be concerned with vision-based monitoring. People may not be tolerated surveillance cameras installed everywhere in our daily life. A camera-based system may not work robustly in unusual situations such as smoke, fogs, or darkness. To cope with these problems, we propose a two-dimensional (2D) LiDAR-based people tracking technique based on clustering algorithms. A LiDAR sensor is a prominent approach for tracking people without disclosing their identity, even under challenging conditions. For tracking people, we propose modified density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify cluster structure (OPTICS) algorithms for clustering 2D LiDAR data. We have confirmed that our approach significantly improves the accuracy and robustness of people tracking through the experiments.

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