Evaluating a bag-of-visual features approach using spatio-temporal features for action recognition. (November 2018)

• Record Type: Journal Article
• Authors: Nazir, Saima; Yousaf, Muhammad Haroon; Velastin, Sergio A.
• Is Part Of: Computers & electrical engineering. Volume 72(2018)
• Page Start: 660
• Page End: 669
• Publication Date: 2018-11
• Subjects: Human action recognition -- Local spatio-temporal features -- Bag-of-visual features -- Hollywood-2 dataset; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.01.037
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The detection of the spatial-temporal interest points has a key role in human action recognition algorithms. This research work aims to exploit the existing strength of bag-of-visual features and presents a method for automatic action recognition in realistic and complex scenarios. This paper provides a better feature representation by combining the benefit of both a well-known feature detector and descriptor i.e. the 3D Harris space-time interest point detector and the 3D Scale-Invariant Feature Transform descriptor. Finally, action videos are represented using a histogram of visual features by following the traditional bag-of-visual feature approach. Apart from video representation, a support vector machine (SVM) classifier is used for training and testing. A large number of experiments show the effectiveness of our method on existing benchmark datasets and shows state-of-the-art performance. This article reports 68.1% mean Average Precision (mAP), 94% and 91.8% average accuracy for Hollywood-2, UCF Sports and KTH datasets respectively.