State-of-the-Art Violence Detection Techniques: A review

Surveillance systems are playing a significant role in law enforcement and city safety. It is important to detect violent and suspicious behaviors automatically in video surveillance scenarios, for instance, railway stations, schools, hospitals to avoid any casualties which could cause social, economic, and ecological damage. Automatic detection of violence for quick actions is very significant and can efficiently help law enforcement departments. So, researchers are doing a lot of research on different techniques for detecting violence. This research study reviews various techniques and methods for detecting violent or anomalous activities from surveillance video that have been proposed by many researchers in recent years. The method of detection is divided into three categories. These categories are based on the classification techniques used. These categories are: traditional violence detection using machine learning, Support Vector Machine (SVM) & Deep Learning. Feature extraction & Object detection techniques are also described for each category. Moreover, dataset & video features that help in the recognition process are also discussed. The overall research finding has been discussed which will help the researcher in their future work in this field. Review Article Biswas et al.; AJRCOS, 13(1): 29-42, 2022; Article no.AJRCOS.79063 30


INTRODUCTION
In different countries around the world, violence is just an everyday incident. As per the ActionAid survey in 2016 [1] [3], street harassment is a prevalent problem and observed that 79% of Indian women, 86% of Thailand women, 89% of Brazil women, and 75% of London women are subjected to harassment or violence publicly. Law enforcement can't stop these incidents from happening as they are unable to take immediate action. The government has already installed a huge amount of surveillance cameras in public places like roads, stations, ports, and schools. But monitoring these surveillance feeds 24 hours is a tiresome job for humans.
Real-world anomalous [4] events are complicated and diverse. It's quite difficult to list all of the possible anomalous events and violence. If it's possible to detect this violence automatically using machine learning and alert the authorities in real-time then the crime rate can be controlled effectively. The loss of lives and properties can be minimized if violence can be detected right away when it happens. The effectiveness of anomalous event detectors is measured by the speed of response and the accuracy and the generality over different kinds of video sources with different formats.
Generally, anomalous events rarely happen as compared to normal day-to-day activities. Therefore, to alleviate the waste of labor and time, developing intelligent computer vision algorithms [4] for automated anomaly detection from video may be a pressing need. As per the researcher, 99% of the generated footage is never watched [6] and hence, detection of suspicious activity is a very difficult task. Once an anomaly is detected, the system should be able to categorize it using classification [7] techniques.
There are a lot of different techniques and methodologies available to detect violent and suspicious behaviors. These methods work with different attributes of videos. The process is done in several steps. The first step is extracting frames from the video, secondly detecting objects, then extracting features of those objects, after that classifies those features [11], and lastly detects the anomaly. These steps vary on different methods and techniques. The basic steps of detecting violence are shown in fig. 1. This process is different among the researchers to increase the accuracy and efficiency of the detection process. We have discussed different methods of violence detection using computer vision.
A lot of research has been done in this field over the past years. We aim to explore this field and present a summarized report in this paper. So, we need to gather all the relevant studies in this field. After that we can conduct a comprehensive research study; classify, analyze & summarize all the proposed methodology. The anomalies [12] activity can be different such as: Abuse, Burglar, Explosion, Shooting, Fighting, Shoplifting, Road Accidents, Arson, Robbery, Stealing, Assault, Vandalism, etc. So, detecting one anomaly activity process can be different from another. Our research contribution and work can be summarized as follows: • Classification of existing models into various categories.

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Describing the main features, limitations of each model.

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Reviewing the importance of video features for violence detection.

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Discussion of different datasets used mostly by the researchers.
The rest of the paper contains five chapters.
Chapter 2 is about the basic concept of violence detection. The research methodology and selection process has been presented in chapter 3. Processes of violence detection are discussed briefly in chapter 4. Chapter 5 contains video features and finally chapter 6 presents the discussion of various datasets. Last chapter presents the conclusion.

BASIC CONCEPT
To understand the process of violence detection, we need to know the basics of violence, computer vision, and machine learning. Some basics and their descriptions are presented in Table 1. Acceleration of Images Change of speed or velocity over the time unit. The field of acceleration consists of two directions x & y.
Researchers use a wide variety of processes and models to recognize an object from a video stream. The method could be different but the basic structure is always the same.

RESEARCH METHODOLOGY
This paper is about presenting the most efficient and effective methods or techniques of violence recognition process available. Classifying the process and review the result of those methods. This study is based on the research paper of researchers collected from various publication sites. These papers had to fill some predefined criteria before being included in this study.

A. Data Acquisition & Selection
The basic target of this paper is to summarize the available methodology in the field of violence and anomalies detection from videos. Then find out the most effective process by comparing the efficiency of each method. To collect relevant studies and papers, a well-organized and sorted search has been done to extract only the relevant and meaningful information from a huge amount of data.
All kinds of irrelevant studies are being filtered out to focus on the relevant field of violence detection and removing the accurate knowledge for a well-organized literature review. To find out accurate and meaningful studies, a strategic plan for searching available studies is one of the most important steps. Two types of searches should be conducted for this kind of study. We have applied both automatic and manual searches.
To find out the relevant study from the digital library, we conducted an automatic search and then a manual search. Automatic search is done by entering the basic strings of these research fields. A manual search was needed for collecting more information in the field of violence and anomaly detection. Then manually searched papers from references to present an accurate review of the study.
These searches are conducted with some major keywords of this field which results in returning the relevant and practical information from the digital library. Those keywords were researched properly to perform the most accurate and reliable research on violence and anomaly detection. These keywords and possible alternate words and the keywords research summary are presented in Table 2. These keywords are used to perform an automated and manual search in the digital data library and websites to write an appropriate review and discover comprehensive data. Various journal publications, conference papers, and other information are collected from the following websites: I. IEEE Xplore II. Google Scholar III. Science-Direct IV. ACM Some prerequisites were set to extract the most relevant study in the field of violence detection techniques. The following prerequisites were set to find out the publication from different digital data libraries: I.
Publication year range should be January 2019 to April 2021. II.
Papers that match the search string are included only. III.
Full-length papers are selected only. IV.
Paper written in English is selected for this study and all the papers are excluded which are written in English.
We have got 170 relevant publications that match all the prerequisites from Google Scholar, 72 from IEEE Xplore, 22 from Science-Direct, and 7 results from ACM. After analyzing the search result and title, abstract and result of those papers, and then filtering out those papers that don't match with the prerequisite. These papers are selected based on the abstract, proposed methodology, video is used as input for detection techniques, different feature extraction techniques, and accuracy of the detection. After going through every step, we have finalized 20 papers for the final study.
Our search techniques & paper selection steps are shown in Fig. 3.
Search results, paper filtering steps, and selection of paper in every step are presented in Table 3. Fig. 4 is about the summarized paper selection process, steps, and filtering criteria of every paper.

Multiple Anomalous Activity Detection
To locate unusual violent behaviors in surveillance cameras is the main task but it requires manpower that works 24/7. That's why this proposed framework consists of two methods that can ease the process. First is determining object direction by measuring if the objects in videos show any kind of movements and the second one is to locate if those movements are usual or non-usual. It is capable of detecting multiple activities in a single video and also can perform behavior understanding. If any kind of anomaly is detected, it will give an alert to the system. Gaussian Mixture Model (GMM) [20] is used for object detection in this framework. The main components that are used in this method are preprocessing phase, feature extraction phase, and recognition phase. In the first phase-the preprocessing, the moving object is detected and noise removal is done. Different features like centroid, movement, speed, direction and dimensions are calculated in the feature extraction phase, and in the last phase, the Rule-based classification method is used to classify the activities of the input video and the alarm is generated for every suspicious activity. This framework suggests that the Rule-Based approach works very handy in detecting multiple activities in a single video.

Anomaly Detection using Bidirectional Prediction
The approaches made in this video are categorized into two sections: probability estimation and frame generation. Anomaly detection approaches based on frame prediction [24,25,30,31] usually use a few previous frames to predict the target frame. Compared with the frame reconstruction approaches [26-31], the frame prediction approaches consider the anomaly not only in appearance and location but also in motion. This method proposes a new loss function that combines the cross-mean square error MSEM as well as the traditional mean square error MSEF and MSEB. The proposed anomaly detection model proposes a simple and elegant structure with fewer parameters, which proves beneficial for network training and easy to be combined with other algorithms and training tricks. Experimental results showed that this model achieves great performance on different datasets, outperforming the most state-of-the-art competing algorithms in detection accuracy, and has good adaptability to different datasets.

Brutality Identification using Haar Cascade Algorithm
In this framework, the Haar Cascade algorithm is used. This method trains first motion vector images by using a machine learning algorithm. The Open CV can be trained the Haar-cascade utility. Using multithreading on the training process reduces the time taken for the training of the data set. This method can discriminate fighting scenes with high true positive rates. The final step is to create a fragment in a straightforward technique and to apply the filter to reduce the noise and to get the final accurate detection of the fragment. As a result, the early detection method is 84.6% accurate when the fight is started.

Violence Detection using Computer Vision and Machine Learning Techniques
A framework has been proposed in this methodology that can detect violent action in sensitive areas by using machine learning and computer vision techniques. It applies six different modules upon the captured videos-

Real-world Anomaly Detection
A small step towards addressing anomaly detection is to develop algorithms to detect a specific anomalous event, for example, a violence detector [32] and a traffic accident detector [33,34]. The framework proposes an approach that divided the videos into a fixed number of segments during the dataset training. These segments then make those instances into two categories one positive (anomalous) and second negative (normal) and train the anomaly detection model using the proposed deep MIL ranking loss. To check the performance of the proposed approach, a new large-scale anomaly dataset that is consisting a variety of real-world anomalies is introduced. The experimental results on this dataset show that the proposed anomaly detection approach performs significantly better than baseline methods.

Fight Detection in Video Sequences
This study uses a deep learning model based on multi-stream and high-level hand-crafted descriptors. In this framework, the main focus is to use a multi-stream of VGG-16 streams to cope with the binary problem and uses the deep neural network to verify the existence of fight detection in videos. A four-stream model can provide the information of which features are relevant to be considered during a binary fight detection problem. In this framework, the method is evaluated on Hockey Fight

Violence Detection using Conv Nets
Most of the time 99% of the generated footage is never watched [36] and because of that detection of suspicious activity is a very difficult task [37], [38]. Researchers are now using computer vision techniques to recognize human activity [39]- [41] and monitor the crowd automatically. The main aim is to present state-of-the-art research in the field of violence detection specifically using CNN. The advantages and shortcomings are pointed out by researching the various CNNs used for violence detection in videos. CNN. The advantages and shortcomings of various CNNs are shown here. The popular available datasets are used to evaluate the proposed models.

Video Anomaly Detection using Inflated 3D Convolution Network
Computer vision [42] is the technology for building artificial systems [55] and it contains methods for data analysis and pattern recognition. It is the process of extracting meaningful information from images and videos and making decisions based on this information. This framework suggests a video surveillance anomaly detection algorithm that works on a weekly labeled dataset. For feature extraction, I3D-Resnet-50 [

Deep Network using Transfer Learning
This research work selected GoogleNet due to deep network architecture with 12 times fewer parameters than AlexNet as a pre-trained model. It uses Hockey and Movies datasets by using transfer learning for creating a deep representation classifier [58] for violent scenes detection. It is also trained using a 10-fold crossvalidation scheme, by developing a dataset images pipeline with images resizing, as input to fine-tuning network for each distinct fold.

Real-World Fight detection
This framework proposed a pipeline for fight detection and the results show that the use of explicit motion information (Optical Flows) has a major positive impact on performance. It is significantly superior to the RGB-only methods. Also, this framework can leverage the information coming from non-CCTV fights, through a 2-tiered model which can generalize better for the CCTV source. Better use of the sequential information at the prediction stage is another interesting aspect since the LSTM [59] failed to leverage this information. Also, it is possible to design Early Detection methods for this scenario as well, considering the importance of quickly detecting that the fight has started.

Hough Forests and 2D Convolutional Neural Network
This framework proposed a hybrid approach. The rich Spatio-temporal voting information from Hough Forests classifier is used to leverage the representative image for each sequence, which is fed with BRISK features that capture motion and appearance from a video sequence. It demonstrates superiority over different 'handcrafted" features [60] and 3D Convolutional Neural Network approaches for this binary recognition task. It provides the best absolute accuracy in two of the three considered datasets with 99%, 94.6%, and 91.4% accuracies in the Movies, Hockey, and Behave datasets.

Big Data Analysis and Deep
Learning through Bidirectional LSTM This proposed framework uses the HOG function to extract the features from the input video in the Spark environment. The frames are labeled into three models. A bidirectional LSTM network is trained to recognize the anomalous events in those models. This system has a 94.5% of accuracy in violence detection rate.

Local Distinguishability Aggrandizing Network for Human Anomaly Detection
The researcher of Local distinguishability aggrandizing network for human anomaly detection analyses how a local distinguishability aggrandizing network (LDA-Net) detects and locates human anomalies in a supervised manner. To validate the effectiveness of LDA-Net, it compares the method with state-ofthe-art methods on the UCSDped2 and subway datasets. For the LDA-Net analysis result, this framework uses frame-level and pixel-level evaluations, and both of them comparisons with state-of-the-art methods on UCSD Ped2 in terms of EER and AUC. The accuracy of Frame-level 97% and Pixel-level 92%.

Deep Learning-based Methods for Video Anomaly Detection
A comprehensive review of deep learning-based methods [61] for video anomaly detection has been presented in this work. The existing deep learning methods are evaluated in terms of datasets, performance metrics, qualitative analysis. The challenges available in deep learning approaches are outlined here.

Violent Event Detection in Visual Surveillance
In this proposed framework, both multiple and monocular cameras were used to detect violence. BEHAVE datasets were used to compare single-temporal frameworks with multitemporal frameworks. The experiments show that the proposed multi-temporal framework produces highly accurate results. The semantic-modeling bases (SGT) were used for visual surveillance.

Multi-stream CNN for Violence Detection using Handcrafted Features
A novel multi-stream CNN is used in this proposed framework to detect abnormal behavior between persons. Various feature descriptors using texture motion [52] and shapes are extracted by handcrafted methods. This proposed architecture is consisted of two handcrafted and deep learning parts for feature extraction and data classification. The CNN network predicts all the input frames of datasets and the accuracy is approximately 100% for both crowded and uncrowded environments. Object detection method, feature extraction [54] method, scene type, and accuracy of every selected literature are presented in Table 4. To understand the methodology and result more effortlessly.

VIDEO FEATURES
Detecting any activity from video requires some elements. The video feature is one of the basic elements for detecting any activity from that video. The detecting process and methodology directly depend on the video features that are extracted from a video. Those features are used to analyze the pattern of the activity. In a video that contains a fight scene, the movement of the objects is faster than the normal video. Analyzing those extracted features classifies the activity of the video. In Table 5, we have presented all the features that are used in the selected study. Temporal & spatial features.

Dataset used
Different researchers used different datasets. These datasets are used to evaluate the performance of the methodology. Table 5 represents the summary of the datasets.

CONCLUSION
These days the crime rate has been increased a lot. Using surveillance cameras is a must for the law enforcement section. But watching these video feeds 24/7 is almost impossible for a human being. So, detecting crime using AI has opened a new era for researchers. Throughout the last decade, a lot of research has been done in the field of violence and anomaly detection.
Researchers have used a lot of detection techniques and methodologies. Many researchers also proposed some new methodologies. The basic goal of this study is to present a systematic review of violence detection techniques. Explore the methodologies used by the researchers and explain the result of each technique. Dataset, video feature, accuracy, etc. which plays important roles in detection techniques are also listed in comprehensive tables. Feature extraction, object detection, classification techniques used on different methodologies are also explored in our study. Our study Contributes to highlighting the violence and anomaly detection methods and techniques from surveillance videos.

ETHICAL APPROVAL
All of the datasets and information are publically available and ethically sourced. There is no ethical issue in this paper.