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Violence detection in surveillance video using low-level features.

Peipei Zhou1,2,3,4, Qinghai Ding1,5, Haibo Luo1,3,4

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This study introduces a new method for detecting violent behaviors in videos by analyzing motion regions using local histograms of oriented gradients and optical flow. The approach significantly improves violence detection accuracy compared to previous methods.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Video Surveillance

Background:

  • Automatic detection of violent behaviors is crucial for public safety in surveillance.
  • Existing methods struggle with effectively detecting violence due to limited feature extraction.
  • Need for advanced techniques to analyze complex motion patterns in video data.

Purpose of the Study:

  • To propose a novel and effective method for detecting violent sequences in video surveillance.
  • To address limitations of previous violence detection approaches.
  • To enhance the accuracy and reliability of automated violence detection systems.

Main Methods:

  • Segmenting motion regions based on optical flow field distribution.
  • Extracting Local Histogram of Oriented Gradient (LHOG) and Local Histogram of Optical Flow (LHOF) features.
  • Utilizing Bag of Words (BoW) for feature coding and Support Vector Machine (SVM) for classification.

Main Results:

  • The proposed method outperforms previous approaches in violence detection.
  • Demonstrated superior performance on three challenging benchmark datasets.
  • Effective extraction of appearance and dynamics features for violent behaviors.

Conclusions:

  • The novel method provides a superior approach to video-based violence detection.
  • The combination of LHOG and LHOF features enhances detection capabilities.
  • This technique offers a promising solution for real-world surveillance applications.