Fusion strategies for recognition of violence actions

Our work highlights event detection system in video surveillance sequences. This should mainly distinguish acts of violence. The survey discusses the current methods and techniques that are being applied for the task of automated violence recognition in the images derived from video surveillance seq...

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Bibliographic Details
Main Authors: Lejmi, Wafa, Ben Khalifa, Anouar, Mahjoub, Mohamed Ali
Format: Conference Proceeding
Language:English
Subjects:
Bag-of-words
Classification
Classification algorithms
Dataset
Decision
Descriptor
Detectors
Extraction
Feature
Feature extraction
Fusion
HARRIS
HOG
Interest Point
KNN
Majority Vote
Optimization
Random Forest
Support vector machines
SURF
SVM
Video surveillance
Violence
Visualization
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Description
Summary:Our work highlights event detection system in video surveillance sequences. This should mainly distinguish acts of violence. The survey discusses the current methods and techniques that are being applied for the task of automated violence recognition in the images derived from video surveillance sequences. To do this, we propose a fusion strategy after using a variety of feature extraction algorithms to obtain the points-of-interest from input images and each of the extracted feature vectors is submitted to a classifier. In a decision fusion strategy, different classifiers are used to classify a feature vector and to establish a most suitable decision to classify the input action as violent or non-violent. We study the performance of the mentioned approaches on 21 datasets of human interaction images. Experiments were implemented in Matlab computing environment. This paper aspires to be a contribution for researchers who wish to improve the study of violent activity recognition and gather inspiration on the main challenges to tackle in this emerging field.
ISSN:2161-5330
DOI:10.1109/AICCSA.2017.193