The technology of 3D recognition is evolving rapidly, enabling unprecedented growth of applications towards human-centric intelligent environments. On top of these applications human segmentation is a key technology towards analyzing and understanding human mobility in those environments. However, existing segmentation techniques rely on deep learning models, which are computationally intensive and data-hungry solutions. This hinders their practical deployment on edge devices in realistic environments. In this paper, we introduce a novel micro-size LiDAR device for understanding human mobility in the surrounding environment. The device is supplied with an on-device lightweight human segmentation technique for the captured 3D point cloud data using density-based clustering. The proposed technique significantly reduces the computational complexity of the clustering algorithm by leveraging the Spatiotemporal relation between consecutive frames. We implemented and evaluated the proposed technique in a real-world environment. The results show that the proposed technique obtains a human segmentation accuracy of 99% with a drastic reduction of the processing time by 66%.

In this paper, we demonstrate a brand new design of a wearable device that enables privacy-preserving human activity recognition based on light-weight compact-size LiDAR. The device scans and represents the surrounding environment in 3D point clouds form. The system further processes this representation to define discriminative features that facilitate recognizing human activity on edge. These features are extracted using Spatio-temporal probabilistic clustering and fisher vector representations and then used to train a classification model for activity recognition purposes. Implementation and evaluation of the proposed system confirm its efficient ability to identify human activities.

This paper presents the design and development of a lightweight, portable 3D spatial sensing device equipped with a compact LiDAR-type sensor. The device provides a 3D point cloud representation of the surrounding environment as captured by the sensor. Based on the acquired 3D point cloud, we propose a real-time object recognition method. The technical challenge is how to process the 3D point data in real-time while pursuing the best trade-off between the processing overhead and recognition accuracy. To answer the question, we leverage the Fisher Vector to extract spatio-temporal features of different objects enabling efficient classification of these objects using the Support Vector Machine approach. The experimental results show that the proposed method achieves mean Average Precision of 0.961. The processing speed on the device was 59.3 frames/second, indicating that object detection can be done in real-time on the device.