A Real-Time Human Action Recognition System Using Depth and Inertial Sensor Fusion

This paper presents a human action recognition system that runs in real time and simultaneously uses a depth camera and an inertial sensor based on a previously developed sensor fusion method. Computationally efficient depth image features and inertial signals features are fed into two computationally efficient collaborative representative classifiers. A decision-level fusion is then performed. The developed real-time system is evaluated using a publicly available multimodal human action recognition data set by considering a comprehensive set of human actions. The overall classification rate of the developed real-time system is shown to be >97%, which is at least 9% higher than when each sensing modality is used individually. The results from both offline and real-time experimentations demonstrate the effectiveness of the system and its real-time throughputs.

[1]  Wanqing Li,et al.  Action recognition based on a bag of 3D points , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[2]  Jintao Li,et al.  Hierarchical spatio-temporal context modeling for action recognition , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Allen Y. Yang,et al.  Distributed recognition of human actions using wearable motion sensor networks , 2009, J. Ambient Intell. Smart Environ..

[4]  Ying Wu,et al.  Robust 3D Action Recognition with Random Occupancy Patterns , 2012, ECCV.

[5]  Qian Du,et al.  Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Nasser Kehtarnavaz,et al.  Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors , 2015, IEEE Transactions on Human-Machine Systems.

[7]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Nasser Kehtarnavaz,et al.  Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition , 2014, IEEE Sensors Journal.

[9]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[10]  Andrés Pérez-Uribe,et al.  Indoor Activity Recognition by Combining One-vs.-All Neural Network Classifiers Exploiting Wearable and Depth Sensors , 2013, IWANN.

[11]  Nasser Kehtarnavaz,et al.  Action Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[12]  Nasser Kehtarnavaz,et al.  A medication adherence monitoring system for pill bottles based on a wearable inertial sensor , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, ICPR 2004.

[14]  Nasser Kehtarnavaz,et al.  Home-based Senior Fitness Test measurement system using collaborative inertial and depth sensors , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[16]  Lei Zhang,et al.  Sparse representation or collaborative representation: Which helps face recognition? , 2011, 2011 International Conference on Computer Vision.

[17]  Nasser Kehtarnavaz,et al.  Real-time human action recognition based on depth motion maps , 2013, Journal of Real-Time Image Processing.

[18]  Tim Dallas,et al.  Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer , 2014, IEEE Transactions on Biomedical Engineering.

[19]  Shashidhar Patil,et al.  Inertial Sensor-Based Touch and Shake Metaphor for Expressive Control of 3D Virtual Avatars , 2015, Sensors.

[20]  Nasser Kehtarnavaz,et al.  UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[21]  Dimitrios Makris,et al.  G3D: A gaming action dataset and real time action recognition evaluation framework , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[22]  Ruzena Bajcsy,et al.  Berkeley MHAD: A comprehensive Multimodal Human Action Database , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).