Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

International Conference on Materials, Alloys and Experimental Mechanics 2017

材料科学;金属学;机械制造

Sanal Kumar, K.P.^1 ; Bhavani, R.^1

Dept. of CSE, Annamalai University, India^1

关键词: Activity recognition;    Combined supports;    Egocentric;    Histogram of oriented gradients;    Histogram of oriented gradients (HOG);    K nearest neighbor (KNN);    Lifelogging;    Motion boundary;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/225/1/012226/pdf
DOI  :  10.1088/1757-899X/225/1/012226

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.