Human Identification Using Video-Based Analysis of the Angle Between Skeletal Joints

İnce, Ömer Faruk; Ince, İbrahim Furkan; Yıldırım, Mustafa Eren; Park, Jang-Sik; Song, Jongkwan

doi:10.5302/j.icros.2018.17.0195

Cited by 1 publication

(2 citation statements)

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“…Additionally, k-NN is a lazy learning scheme, and only stores the input data during the training process; thereby, training is essentially spontaneous [51]. For instance, RF produces an in-memory classification model which does not require database lookups, while k-NN uses on-the-spot learning that requires extensive computations, which makes k-NN inefficient for classifying large databases.…”

Section: Comparison Of Different Classifiersmentioning

confidence: 99%

“…Apart from this, eager learners such as RF cannot easily model decision spaces with complicated decision boundaries; in contrast, k-NN performs instance-based learning which leads to accurate performance if a well-tuned k-NN model is used. Additionally, k-NN is a lazy learning scheme, and only stores the input data during the training process; thereby, training is essentially spontaneous [51]. The parameters used for k-NN were given in the previous section.…”

Section: Comparison Of Different Classifiersmentioning

confidence: 99%

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Human activity recognition with analysis of angles between skeletal joints using a RGB‐depth sensor

et al. 2019

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Human activity recognition (HAR) has become effective as a computer vision tool for video surveillance systems. In this paper, a novel biometric system that can detect human activities in 3D space is proposed. In order to implement HAR, joint angles obtained using an RGB-depth sensor are used as features. Because HAR is operated in the time domain, angle information is stored using the sliding kernel method.Haar-wavelet transform (HWT) is applied to preserve the information of the features before reducing the data dimension. Dimension reduction using an averaging algorithm is also applied to decrease the computational cost, which provides faster performance while maintaining high accuracy. Before the classification, a proposed thresholding method with inverse HWT is conducted to extract the final feature set.Finally, the K-nearest neighbor (k-NN) algorithm is used to recognize the activity with respect to the given data. The method compares favorably with the results using other machine learning algorithms. K E Y W O R D Sactivity recognition, dimension reduction, Haar-wavelet transform, K-nearest neighbour, RGB-D sensor | 89 İNCE Et al.

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Section: Comparison Of Different Classifiersmentioning

confidence: 99%

Section: Comparison Of Different Classifiersmentioning

confidence: 99%