Depth features to recognise dyadic interactions
| Autor: | Ali Seydi Keceli, Ahmet Burak Can, Aydin Kaya |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Rank (linear algebra)
business.industry Computer science Feature extraction Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Ranking (information retrieval) Random forest Activity recognition Support vector machine Gesture recognition Histogram 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software 0105 earth and related environmental sciences |
| Zdroj: | IET Computer Vision. 12:331-339 |
| ISSN: | 1751-9640 |
| DOI: | 10.1049/iet-cvi.2017.0204 |
| Popis: | Usage of depth sensors in activity recognition is an emerging technology in human-computer interaction. This study presents an approach to recognise human-to-human interactions by using depth information. Both hand-crafted features and deep features extracted from depth frames are studied. After selecting and ranking strong features with Relieff algorithm, depth frames are assigned to words. Then, interaction sequences are represented as histograms of words and non-linear input mapping is applied over histogram bins to minimise differences among various subjects. Random forest, K-nearest neighbour, and support vector machine (SVM) classifiers are trained using these histograms. The final model is tested on SBU and K3HI datasets and compared with the methods in the literature. In the experiments, joint distances, joint angles and spherical coordinates of the joints were the best performing features. The most successful results are obtained with the composite kernel SVM with Relieff and input mapping methods. While Relieff algorithm helps to select and rank the best features in the feature set, input mapping reduces differences among interactions of various actors. |
| Databáze: | OpenAIRE |
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