Contextual Action Cues from Camera Sensor for Multi-Stream Action Recognition

Human Action Recognition from Multi-Sensor Stream Data by Genetic Programming

This paper presents an approach to recognition of human actions such as sitting, standing, walking or running by analysing the data produced by the sensors of a smart phone. The data comes as streams of parallel time series from 21 sensors. We have used genetic programming to evolve detectors for a number of actions and compared the detection accuracy of the evolved detectors with detectors bui...

Encoding Multi-resolution Two-Stream CNNs for Action Recognition

Multi-sensor Acceleration-Based Action Recognition

In this paper, a framework to recognize human actions from acceleration data is proposed. An important step for an accurate recognition is the preprocessing of input data and the following classification by the machine learning algorithm. In this paper, we suggest to combine Dynamic Time Warping (DTW) with Random Forest. The intention of using DTW is to pre-process the data to eliminate outlier...

Identifying Kinematic Cues for Action Style Recognition

Recognition of emotional states from other’s actions is one of key capability for smooth social interaction. The present study provides a computational-theory-level analysis on which feature may take a crucial role in recognition of emotional attributes in human actions represented as point-light display. Lead by the previous theoretical works and empirical findings, the velocity and accelerati...

Learning multi-level features for sensor-based human action recognition

This paper proposes a multi-level feature learning framework for human action recognition using body-worn inertial sensors. The framework consists of three phases, respectively designed to analyze signal-based (low-level), components (mid-level) and semantic (high-level) information. Low-level features, extracted from raw signals, capture the time and frequency domain property while mid-level r...