Ms. Rawya Al-Akam Profile

Rawya Al-Akam

at Univ Koblenz-Landau

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Proceedings Article | 15 March 2019 Paper

Comparison of different machine learning models on feature extraction for human activity recognition from RGB-depth datasets

Rawya Al-Akam, Dietrich Paulus

Proceedings Volume 11041, 1104128 (2019) https://doi.org/10.1117/12.2522680

KEYWORDS: Video, Feature extraction, Optical flow, Machine learning, Data modeling, RGB color model, Matrices, Detection and tracking algorithms, Artificial neural networks, Machine vision

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Recognizing human body activities from the video sequences are directly depends on the features extraction for motion analysis, which is each activity can be presented by certain motion features. Therefore, by using corresponding features, we can probably classify different activities. This idea inspires us to form the activity recognition as a classification problem and verify its feasibility. In this work, two important goals are presented. The first one is extracting the motion and texture features from RGBD sequences by proposing a feature extracting method to extract feature vector values based on the Gray-Level Co-occurrence matrices (GLCM) of the dense optical flow pattern and the well-known Haralick features from these matrices by measuring meaningful properties such as energy, contrast, homogeneity, entropy, sum average, and correlation to capture local spatial and temporal characteristics of the motion through the neighboring optical flow fields (orientation and magnitude). Secondly, we present a performance comparison of five different classifiers such as Artificial Neural Networks, Naive Bayes classifier, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Various numerical experiments results are carried on four well-known public datasets (Gaming Datasets, Cornell Activity Datasets, MSR Daily Activity 3D and Online RGBD Datasets) to verify the effectiveness of these classification algorithms. From experiments, the classifiers show different performance according to the features that computed and the set of classes from different activities. And the results demonstrate that all the five algorithms achieve satisfactory activity recognition performance.

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