Human action recognition using fusion of features for unconstrained video sequences. (August 2018)

• Record Type: Journal Article
• Authors: Patel, Chirag I; Garg, Sanjay; Zaveri, Tanish; Banerjee, Asim; Patel, Ripal
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 284
• Page End: 301
• Publication Date: 2018-08
• Subjects: Human action recognition -- Histogram of oriented gradient (HOG) -- Local binary pattern (LBP) -- Artificial neural network -- Support vector machine -- Multiple kernel learning -- Decision combination neural network (DCNN) -- Choquet's fuzzy integral (CFI) -- Decison template; 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.2016.06.004
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: In order to reduce the size of the feature descriptor, it is built using overlapping window concept. Feature descriptor has been enriched by incorporating displacement and velocity features for effective Human Action Recognition. Exploit early and late fusion for enhancing the performance of the Human action recognition in constrained video sequences. Graphical abstract: Abstract: Effective modeling of the human action using different features is a critical task for human action recognition; hence, the fusion of features concept has been used in our proposed work. By fusing several modalities, features, or classifier decision scores, we present six different fusion models inspired by the early fusion schemes, late fusion schemes, and intermediate fusion schemes. In the first two models, we have utilized early fusion technique. The third and fourth models exploit intermediate fusion techniques. In the fourth model, we confront a kernel-based fusion scheme, which takes advantage of kernel basis of classifiers i.e. Support Vector Machine (SVM). In the fifth and sixth models, we have demonstrated late fusion techniques. The performance of all models is evaluated with ASLAN and UCF11 benchmark dataset of action videos. We obtained significant improvements with the proposed fusion schemes relative to the usual fusion schemes relative state-of-the-art methods.