Huoyuan Tan

Hi, I’m Huoyuan Tan, a second-year Master’s student in Computer Science and Engineering at the IT Faculty of the University of Gothenburg. I have my Bachelor’s degree in Medical Information Engineering and under the supervision of my advisor, I participated in the analysis of data collected from biological IoT devices using machine learning.

After graduation, I worked as an algorithm engineer at an autonomous driving company, Xingyun Intelligence (Shenzhen) Technology Co., Ltd., where I developed algorithms related to industrial autonomous systems, including SLAM, perception, path planning, and control.

My research interests lie in Autonomous Driving, Vision-Language-Action mode(VLA), Large Language Models(LLMs) and Reinforcement Learning. I have hands-on experience with C++, Python, and Linux, and I am passionate about technology and motivated by the belief that technology can enhance people’s well-being, reduce emissions, and contribute to a more sustainable future. I am currently seeking opportunities in the fields of robotics, autonomous driving and LLMs.

selected publications

Abnormal Activity Detection Using Pyroelectric Infrared Sensors

Xiaomu Luo, Huoyuan Tan, Qiuju Guan, and 3 more authors

Sensors, 2016

Abs DOI

Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process.
Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors

Xiaomu Luo, Qiuju Guan, Huoyuan Tan, and 3 more authors

Sensors, 2017

Abs DOI

Indoor human tracking and activity recognition are fundamental yet coherent problems for ambient assistive living. In this paper, we propose a method to address these two critical issues simultaneously. We construct a wireless sensor network (WSN), and the sensor nodes within WSN consist of pyroelectric infrared (PIR) sensor arrays. To capture the tempo-spatial information of the human target, the field of view (FOV) of each PIR sensor is modulated by masks. A modified partial filter algorithm is utilized to decode the location of the human target. To exploit the synergy between the location and activity, we design a two-layer random forest (RF) classifier. The initial activity recognition result of the first layer is refined by the second layer RF by incorporating various effective features. We conducted experiments in a mock apartment. The mean localization error of our system is about 0.85 m. For five kinds of daily activities, the mean accuracy for 10-fold cross-validation is above 92%. The encouraging results indicate the effectiveness of our system.