Abstract
The majority of methods for recognizing human actions are based on single-view video or multi-camera data. In this paper, we propose a novel multi-surface video analysis strategy. The video can be expressed as three-surface motion feature (3SMF) and spatio-temporal interest feature. 3SMF is extracted from the motion history image in three different video surfaces: horizontal–vertical, horizontal- and vertical-time surface. In contrast to several previous studies, the prior probability is estimated by 3SMF rather than using a uniform distribution. Finally, we model the relationship score between each video and action as a probability inference to bridge the feature descriptors and action categories. We demonstrate our methods by comparing them to several state-of-the-arts action recognition benchmarks.
Highlights
Human action recognition in video sequences is a challenging research topic in computer vision (Aggarwal and Ryoo 2011; Poppe 2010) and serves as a fundamental component of several existing applications such as video surveillance human computer interaction, multimedia event detection and video retrieval
Extensive efforts have been devoted to action recognition, including: finding a robust, stable, discrimination feature to represent the action/video, such as the motion history image (MHI), motion trajectories of human bodies (Yoon et al 2014), and spatio-temporal interest point (STIP) (Dawn and Shaikh 2015), and using effective machine learning or pattern recognition methods to identify human action, such as latent support vector machine (SVM) (Zhou et al 2015), deep learning (Charalampous and Gasteratos 2014) and statistical methods
We propose to integrate the holistic features and STIP features into an action classifier
Summary
Human action recognition in video sequences is a challenging research topic in computer vision (Aggarwal and Ryoo 2011; Poppe 2010) and serves as a fundamental component of several existing applications such as video surveillance human computer interaction, multimedia event detection and video retrieval. Related work The numerous existing methods for recognizing human action from image sequences or video have been classified as template-based approaches, local-feature-based approaches (including appearance and motion features) and object/scene-context-based approaches. The MHI approach is a view-based temporal template method that is simple yet robust in representing movements and has been widely employed by many researchers for human action recognition, motion analysis and other related applications. The intensive computation for NBNN is to search nearest neighbors from training data for all of the STIP features extracted from test video. The computational complexity of the proposed is combination of feature detection and nearest neighbors searching in training data. Due to the STIP set of training data is large, and the method of 3SMF detection, the proposed method is not suitable for real-time recognition
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