Abstract-Human action recognition is an important research area which has captured lot of attention from the research community due to its significant applications. Recently, due to the popularity and successful implementation of deep learning-based methods for image analysis, object recognition, and speech recognition. Researchers are motivated to shift from traditional feature-based approach to deep learning. This research work presents an innovative method for human action recognition using pre-trained Convolutional Neural Networks (CNNs) model as a source architecture for extracting features from the target dataset, followed by a hybrid Support Vector Machines and K-Nearest Neighbor (SVM-KNN) classifier for action classification. It has been observed that already learnt CNN based representations on large-scale annotated dataset are successfully transferable to action recognition task with limited training dataset. The proposed method is evaluated on two well-known action datasets, i.e., UCF sports and KTH. The comparative analysis suggests that the proposed method is better than handcrafted feature-based methods in terms of accuracy.
Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.
This paper presents a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. To achieve this, the human silhouette is divided into regions in a radial fashion with the interval of a certain degree, and then region-based geometrical and Hu-moments features are obtained from each radial bin to articulate the feature descriptor. A multiclass support vector machine classifier is used for action classification. The proposed approach is quite simple and achieves state-of-the-art results without compromising the efficiency of the recognition process. Our contribution is two-fold. Firstly, our approach achieves high recognition accuracy with simple silhouette-based representation. Secondly, the average testing time for our approach is 34 frames per second, which is much higher than the existing methods and shows its suitability for real-time applications. The extensive experiments on a well-known multiview IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset confirmed the superior performance of our method as compared to similar state-of-the-art methods.
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