Recognizing human actions using novel space-time volume binary patterns

Baumann, Florian; Ehlers, Arne; Rosenhahn, Bodo; Liao, Jie

doi:10.1016/j.neucom.2015.03.097

Cited by 40 publications

(24 citation statements)

References 38 publications

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“…From a computational cost point of view, SGSH Method on KTH Accuracy (%) Al Ghamdi et al [27] 90.7 Liu et al [28] 91.3 Iosifidis et al [29] 92.1 Baumann et al [30] 92.1 Kläser [31] 92.6 Ji et al [32] 93.1 Wang et al [33] 94.2 Wu et al [34] 94.5 Raptis and Soatto [35] 94.8 Zhang et al [5] 94.8 Wang et al [21] 95.0 Yuan et al [4] 95.4 SGSH 97.2 Table 3 Recognition accuracy comparisons on the UCF-Sports dataset…”

Section: Running Timementioning

confidence: 99%

Saliency guided local and global descriptors for effective action recognition

2016

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This paper presents a novel framework for human action recognition based on salient object detection and a new combination of local and global descriptors. We first detect salient objects in video frames and only extract features for such objects. We then use a simple strategy to identify and process only those video frames that contain salient objects. Processing salient objects instead of all frames not only makes the algorithm more efficient, but more importantly also suppresses the interference of background pixels. We combine this approach with a new combination of local and global descriptors, namely 3D-SIFT and histograms of oriented optical flow (HOOF), respectively. The resulting saliency guided 3D-SIFT-HOOF (SGSH) feature is used along with a multi-class support vector machine (SVM) classifier for human action recognition. Experiments conducted on the standard KTH and UCF-Sports action benchmarks show that our new method outperforms the competing state-of-the-art spatiotemporal feature-based human action recognition methods.

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Section: Running Timementioning

confidence: 99%

Saliency guided local and global descriptors for effective action recognition

2016

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“…In the learning processes, a lateral reserve term that eliminates competing pattern activations provide small sparse activations [149]. An interaction of motion and spatial information was presented using Motion Binary Pattern (MBP) and Volume Local Binary Pattern (VLBP) with Optical Flow for recognizing human actions and space-time volume binary patterns [150]. A fuzzy multiplication of form and motion information is proposed by combining active basis model with optical flow [38,100,101].…”

Section: Interaction Between Pathwaysmentioning

confidence: 99%

Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

Yousefi

Loo

2019

Electronics

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Theoretical neuroscience investigation shows valuable information on the mechanism for recognizing the biological movements in the mammalian visual system. This involves many different fields of researches such as psychological, neurophysiology, neuro-psychological, computer vision, and artificial intelligence (AI). The research on these areas provided massive information and plausible computational models. Here, a review on this subject is presented. This paper describes different perspective to look at this task including action perception, computational and knowledge based modeling, psychological, and neuroscience approaches.

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“…In [105], a multi-view human action recognition method was proposed based on contour-based pose features and uniform rotation-invariant LBP followed by SVM for classification. Recently, another motion descriptor named Motion Binary Pattern (MBP) was introduced for multi-view action recognition [106]. This descriptor is combination of Volume Local Binary Pattern (VLBP) and optical flow.…”

Section: Local Binary Pattern (Lbp)-based Approachesmentioning

confidence: 99%

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

2017

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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.

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Recognizing human actions using novel space-time volume binary patterns

Cited by 40 publications

References 38 publications

Saliency guided local and global descriptors for effective action recognition

Saliency guided local and global descriptors for effective action recognition

Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

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