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

Yu, Jing; Song, Wei; Zhou, Gongjian; Hou, Jianjun

doi:10.1007/s11042-018-6923-3

Cited by 23 publications

(10 citation statements)

References 21 publications

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“…They also examined different scale features that quantitatively determine the films mood in terms of energetic arousal, hedonic tone, and tense arousal. J. Yu et al [27] proposed an algorithm that detects the violent scenes in the videos. They constructed three novel feature approaches, including bag of visual words (BoVW) model, feature pooling technology, and dimensional histograms of gradient orientation (HOG3D).…”

Section: Introductionmentioning

confidence: 99%

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

et al. 2019

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Movies have become one of the major sources of entertainment in the current era, which are based on diverse ideas. Action movies have received the most attention in last few years, which contain violent scenes, because it is one of the undesirable features for some individuals that is used to create charm and fantasy. However, these violent scenes have had a negative impact on kids, and they are not comfortable even for mature age people. The best way to stop under aged people from watching violent scenes in movies is to eliminate these scenes. In this paper, we proposed a violence detection scheme for movies that is comprised of three steps. First, the entire movie is segmented into shots, and then a representative frame from each shot is selected based on the level of saliency. Next, these selected frames are passed from a light-weight deep learning model, which is fine-tuned using a transfer learning approach to classify violence and non-violence shots in a movie. Finally, all the non-violence scenes are merged in a sequence to generate a violence-free movie that can be watched by children and as well violence paranoid people. The proposed model is evaluated on three violence benchmark datasets, and it is experimentally proved that the proposed scheme provides a fast and accurate detection of violent scenes in movies compared to the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

et al. 2019

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“…Their scheme was fast enough for real applications. Yu and Song [210] developed an automatic violent scene detection system based on KELM. 3D histogram of gradient orientation (HOG) was used as the feature extractor and visual words were generated by K means clustering.…”

Section: Other Interdisciplinary Application Fieldsmentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

275

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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“…Still, the performance is poor in crowded scenes where many people are involved in violent action. An extension of HOG for threedimensional data based on HOG3D is proposed by Yu et al [31]. The HOG3D descriptors are extracted from video blocks; then, these descriptors are encoded following a BoVW framework.…”

Section: Hand-crafted Featuresmentioning

confidence: 99%

Weakly Supervised Violence Detection in Surveillance Video

Roman

Cámara-Chávez

2022

Sensors

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Automatic violence detection in video surveillance is essential for social and personal security. Monitoring the large number of surveillance cameras used in public and private areas is challenging for human operators. The manual nature of this task significantly increases the possibility of ignoring important events due to human limitations when paying attention to multiple targets at a time. Researchers have proposed several methods to detect violent events automatically to overcome this problem. So far, most previous studies have focused only on classifying short clips without performing spatial localization. In this work, we tackle this problem by proposing a weakly supervised method to detect spatially and temporarily violent actions in surveillance videos using only video-level labels. The proposed method follows a Fast-RCNN style architecture, that has been temporally extended. First, we generate spatiotemporal proposals (action tubes) leveraging pre-trained person detectors, motion appearance (dynamic images), and tracking algorithms. Then, given an input video and the action proposals, we extract spatiotemporal features using deep neural networks. Finally, a classifier based on multiple-instance learning is trained to label each action tube as violent or non-violent. We obtain similar results to the state of the art in three public databases Hockey Fight, RLVSD, and RWF-2000, achieving an accuracy of 97.3%, 92.88%, 88.7%, respectively.

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

Cited by 23 publications

References 21 publications

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

A review on extreme learning machine

Weakly Supervised Violence Detection in Surveillance Video

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