Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance. Prior research have explored various "end-to-end" models; however these methods still require several preprocessing steps. These additional operations are often non-trivial to implement making replication and deployment difficult and can even have a higher computational budget than the "core" network itself. In this paper, we propose two novel and efficient neural models for camera-based physiological measurement called EfficientPhys that remove the need for face detection, segmentation, normalization, color space transformation or any other preprocessing steps. Using an input of raw video frames, our models achieve strong performance on three public datasets. We show that this is the case whether using a transformer or convolutional backbone. We further evaluate the latency of the proposed networks and show that our most light weight network also achieves a 33% improvement in efficiency.
Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；Nanjing Forestry University（南京林业大学）
MapFusion: A Novel BEV Feature Fusion Network for Multi-modal Map Construction
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Pennsylvania State University（宾夕法尼亚州立大学）；Department of Biomedical Engineering, Hefei University of Technology（合肥工业大学生物医学工程系）
Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba
MICS Thrust at the Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）MICS分支）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；School of Microelectronics at Southern University of Science and Technology（南方科技大学微电子学院）
Benchmarking Microsaccade Recognition with Event Cameras: A Novel Dataset and Evaluation
C3I Imaging Lab, School of Engineering, University of Galway, Ireland（C3I成像实验室，工程学院，Galway大学，爱尔兰）；School of Computer Science, University of Galway, Ireland（计算机科学学院，Galway大学，爱尔兰）；Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland（电子与电气工程系，Trinity College Dublin大学，爱尔兰）
