An efficient method to resolve intraclass variability using highly refined HOG description model for human action recognition. (25th October 2018)

• Record Type: Journal Article
• Authors: K, Akila; S, Chitrakala
• Other Names: Sundhararajan guestEditor.; Bhuiyan Md Zakirul Alam guestEditor.; Li Xiong guestEditor.; Rawal Bharat S. guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 12(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-25
• Subjects: HOG descriptor -- object detection -- PCA transform -- scale‐invariance; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4856
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Recognizing the human action is an interesting field and promising area of research due to its importance in various applications. HAR system seeks to interpret the actions being accomplished by the human within the video sequence instinctively and tagging their actions for the primary need of an intelligent video system. Recently, the research attention becomes more focused on recognizing the human actions in unrestrained videos, as the variations in scale, illumination, etc, are often will be the case for performance degradation. Hence, we introduced a novel refined gradient model to normalize the HOG descriptor for the scale‐invariant and appearance modeling. In addition, as different subjects can perform the same action in a different manner. Their different appearance or style variation leads to errors. To resolve this Intraclass variability of actions a discriminative approach has been proposed to explore action attributes. Thus, this system unites these two to reduce the discrimination error. This new approach gives near‐perfect ways for reducing overall dense trajectory displacements with significant accuracy enhancements. The complete action recognition system was evaluated on a number of test videos from real‐world data sets and compared against state‐of‐the‐art methods.