Violent scene detection algorithm based on kernel extreme learning machine and three-dimensional histograms of gradient orientation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12-07

AUTHORS

Jing Yu, Wei Song, Guozhu Zhou, Jian-jun Hou

ABSTRACT

Most existing feature descriptors for video have limited representation ability. In order to improve the recognition accuracy of method for detecting the videos that include violent scenes and take advantage of the logical structure of video sequences, a novel feature constructing approach based on three dimensional histograms of gradient orientation (HOG3D), the Bag of Visual Words (BoVW) model, and feature pooling technology is proposed. This approach, combined with kernel extreme learning machine (KELM), can be used to detect violent scene. First, the HOG3D feature is extracted on the block level for video, and then the K-Means clustering algorithm is implemented to generate visual words. Then, the bag of visual words framework is used for the quantization of feature. And the feature pooling technology is operated to generate a feature vector for an entire video segment, and feature vectors of training data and testing data were used separately to train the model and evaluate the performance of the proposed approach. The experimental results showed that the proposed feature descriptor had good representation and generalization abilities. The proposed approach is efficient for violent scene detection, and the accuracy matches the best result on Hockey dataset, and it outperforms state-of-the-art on Movies. More... »

PAGES

1-16

References to SciGraph publications

  • 2014. Violence Detection in Video by Using 3D Convolutional Neural Networks in ADVANCES IN VISUAL COMPUTING
  • 2016-06. A new method for violence detection in surveillance scenes in MULTIMEDIA TOOLS AND APPLICATIONS
  • 2017-03. Evaluation of multiple features for violent scenes detection in MULTIMEDIA TOOLS AND APPLICATIONS
  • 2008. Detecting Violent Scenes in Movies by Auditory and Visual Cues in ADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2008
  • 2015-05. Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience in JOURNAL OF CENTRAL SOUTH UNIVERSITY
  • 2015-02. Human-level control through deep reinforcement learning in NATURE
  • 2013-11. Human action recognition based on chaotic invariants in JOURNAL OF CENTRAL SOUTH UNIVERSITY
  • 2011. Violence Detection in Video Using Computer Vision Techniques in COMPUTER ANALYSIS OF IMAGES AND PATTERNS
  • 2017-01. MoWLD: a robust motion image descriptor for violence detection in MULTIMEDIA TOOLS AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11042-018-6923-3

    DOI

    http://dx.doi.org/10.1007/s11042-018-6923-3

    DIMENSIONS

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