Spatio-temporal feature using optical flow based distribution for violence detection

Mabrouk, Amira Ben; Zagrouba, Ezzeddine

doi:10.1016/j.patrec.2017.04.015

Cited by 62 publications

(24 citation statements)

References 10 publications

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“…The violent flow descriptor then classifies the behaviors based on the support vector machine. Mabrouk A.B et al [15] used a new descriptor to recognized violent scenes based on the magnitude and the orientation of the frame of interest. These feature descriptors show better results on the classification of crowded and non-crowded scenes.…”

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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“…The pervasiveness of video surveillance cameras and the need of watching footages and making decisions in a very short time [1] boosted the interest of researchers towards techniques for the automatic detection of violence and crimes in videos. In facts, both techniques based on handcrafted features [ 2 , 3 ] and deep learning [ 4 , 5 ] demonstrated their accuracy for automatic violence detection on open datasets such as the Hockey Fight Dataset [6] , the Movie Fight Dataset [6] , and the Crowd Violence Dataset [7] . However, such datasets include few low-res videos, sometimes in too specific environments (e.g.…”

Section: Data Descriptionmentioning

confidence: 99%

A dataset for automatic violence detection in videos

et al. 2020

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“…In [31], the spatiotemporal information and slow feature analysis method are combined to represent the discriminative information in videos to detect abnormal crowd motion, but the high semantic inherent features of this method have limited ability to represent nonlinear features. The work in [32] proposes distribution of magnitude and orientation of local interest frame descriptor is used to learn a support vector machine based a binary classifier to detect violence events. Moreover, a feature descriptor is proposed by adopting the covariance matrix coding optical flow in multi-regions of interest to represent motion information, and then one-class support vector machine is applied to detect the abnormal events in [33].…”

Section: A Hand-crafted Features-based Modelsmentioning

confidence: 99%

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Deng

et al. 2020

IEEE Access

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At present, the existing abnormal event detection models based on deep learning mainly focus on data represented by a vectorial form, which pay little attention to the impact of the internal structure characteristics of feature vector. In addition, a single classifier is difficult to ensure the accuracy of classification. In order to address the above issues, we propose an abnormal event detection hybrid modulation method via feature expectation subgraph calibrating classification in video surveillance scenes in this paper. Our main contribution is to calibrate the classification of a single classifier by constructing feature expectation subgraphs. First, we employ convolutional neural network and long short-term memory models to extract the spatiotemporal features of video frame, and then construct the feature expectation subgraph for each key frame of every video, which could be used to capture the internal sequential and topological relational characteristics of structured feature vector. Second, we project expectation subgraphs on the sparse vector to combine with a support vector classifier to calibrate the results of a linear support vector classifier. Finally, the experiments on a common dataset named UCSDped1 and a coal mining video dataset in comparison with some existing works demonstrate that the performance of the proposed method is better than several the state-of-the-art approaches. INDEX TERMS Abnormal event detection, feature expectation subgraph, calibrating classification, sequential and topological relational characteristics.

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Spatio-temporal feature using optical flow based distribution for violence detection

Cited by 62 publications

References 10 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 dataset for automatic violence detection in videos

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

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