Violence Detection in Video by Using 3D Convolutional Neural Networks

2014 ◽  
pp. 551-558 ◽  
Author(s):  
Chunhui Ding ◽  
Shouke Fan ◽  
Ming Zhu ◽  
Weiguo Feng ◽  
Baozhi Jia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Violence Detection

scholarly journals Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection

2021 ◽  
Vol 24 (67) ◽  
pp. 40-50
Author(s):  
Jean Phelipe de Oliveira Lima ◽  
Carlos Maurí­cio Seródio Figueiredo
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Smart Cities ◽  
Video Classification ◽  
Feedforward Networks ◽  
Violence Detection ◽  
Data Representations ◽  
Intelligent Models ◽  
Fusion Approach

In modern smart cities, there is a quest for the highest level of integration and automation service. In the surveillance sector, one of the main challenges is to automate the analysis of videos in real-time to identify critical situations. This paper presents intelligent models based on Convolutional Neural Networks (in which the MobileNet, InceptionV3 and VGG16 networks had used), LSTM networks and feedforward networks for the task of classifying videos under the classes "Violence" and "Non-Violence", using for this the RLVS database. Different data representations held used according to the Temporal Fusion techniques. The best outcome achieved was Accuracy and F1-Score of 0.91, a higher result compared to those found in similar researches for works conducted on the same database.

Violence Detection in Videos by Combining 3D Convolutional Neural Networks and Support Vector Machines

2020 ◽  
Vol 34 (4) ◽  
pp. 329-344 ◽  
Author(s):  
Simone Accattoli ◽  
Paolo Sernani ◽  
Nicola Falcionelli ◽  
Dagmawi Neway Mekuria ◽  
Aldo Franco Dragoni
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Violence Detection ◽  
Vector Machines

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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