Violence recognition is challenging since recognition must be performed on videos acquired by a lot of surveillance cameras at any time or place. It should make reliable detections in real time and inform surveillance personnel promptly when violent crimes take place. Therefore, we focus on efficient violence recognition for real-time and on-device operation, for easy expansion into a surveillance system with numerous cameras. In this paper, we propose a novel violence detection pipeline that can be combined with the conventional 2-dimensional Convolutional Neural Networks (2D CNNs). In particular, frame-grouping is proposed to give the 2D CNNs the ability to learn spatio-temporal representations in videos. It is a simple processing method to average the channels of input frames and group three consecutive channel-averaged frames as an input of the 2D CNNs. Furthermore, we present spatial and temporal attention modules that are lightweight but consistently improve the performance of violence recognition. The spatial attention module named Motion Saliency Map (MSM) can capture salient regions of feature maps derived from the motion boundaries using the difference between consecutive frames. The temporal attention module called Temporal Squeeze-and-Excitation (T-SE) block can inherently highlight the time periods that are correlated with a target event. Our proposed pipeline brings significant performance improvements compared to the 2D CNNs followed by the Long Short-Term Memory (LSTM) and much less computational complexity than existing 3D-CNN-based methods. In particular, MobileNetV3 and EfficientNet-B0 with our proposed modules achieved state-of-the-art performance on six different violence datasets. Our codes are available at https://github.com/ahstarwab/Violence_Detection .

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Efficient Spatio-Temporal Modeling Methods for Real-Time Violence Recognition

To have a better response towards criminal activity, it is very important that one should understand the patterns in crime. We analyze this pattern by taking crime datasets from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. This dataset includes different blocks of the city of Chicago. The major aim of this mission is to expect which category of crime is most probably to take place at a detailed time and places in Chicago. Finally, this paper uses a different algorithm like Random Forest, Decision Tree and different ensemble methods such as Extra Trees, Bagging and AdaBoost to evaluate the accuracy given by each algorithm.

Predicting Crime Using Time and Location Data

Artificial Intelligence & Crime Prediction: A Systematic Literature Review

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. The system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining, real time and location data, the system can extract unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining, real time and location data procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc. we are focusing mainly on crime factors of each day. To have a better response towards criminal activity, it is very important that one should understand the patterns in crime. I analyses these patterns by taking crime datasets from the Nigeria Police zone six (6) Calabar, Cross River State and town planning bodies. This dataset includes different streets of the city of Calabar. The major aim of this mission is to expect which category of crime is most probably to take place at a detailed time and places in

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An overview of crime analysis, prevention and prediction using data mining based on real time and location data

Crimes are a worldwide problem that can harm the nation under both social and economic conditions. Crime control is an inescapable step. It is compulsory for the welfare and sustainable development of a nation. We know very well that in the digital world, it is not an easy task to expose the criminals and the vulnerable areas that are continuously getting affected by their wrongdoings. The police departments of every nation are also continuously working in a paced manner to overcome the crimes, criminals and their techniques. The difficulty in investigating a large amount of data regarding crimes and criminals has become a major challenge for police department officials. An approach is needed that can classify, systematically investigate and forecast the crimes that help to reduce the crime rate. There are various methodologies and paradigms which will help police officials to discover and eliminate crimes from society. Data mining empowers us with several practical and convenient ways to assess large and distinct sets of information. It helps to uncover hidden information from the large database of criminal records for investigating, controlling and preventing crime for organizations and users. Various researchers and data analysts gave their valuable time and knowledge to the field of data mining. The paper focuses primarily on presenting a short and snappy overview of various research papers focused on the techniques of data mining that have been applied in crime analysis.

Data Mining in Crime Analysis

Crime is one of the dominant and alarming aspect of our society. Everyday huge number of crimes are committed, these frequent crimes have made the lives of common citizens restless. So, preventing the crime from occurring is a vital task. In the recent time, it is seen that artificial intelligence has shown its importance in almost all the field and crime prediction is one of them.However, it is needed to maintain a proper database of the crime that has occurred as this information can be used for future reference. The ability to predict the crime which can occur in future can help the law enforcement agencies in preventing the crime before it occurs. The capability to predict any crime on the basis of time, location and so on can help in providing useful information to law enforcement from strategical perspective. However, predicting the crime accurately is a challenging task because crimes are increasing at an alarming rate. Thus, the crime prediction and analysis methods are very important to detect the future crimes and reduce them. In Recent time, many researchers have conducted experiments to predict the crimes using various machine learning methods and particular inputs. For crime prediction, KNN, Decision trees and some other algorithms are used. The main purpose is to highlight the worth and effectiveness of machine learning in predicting violent crimes occurring in a particular region in such a way that it can be used by police to reduce crime rates in the society.

Pratibha

Papers

Crime Prediction and Analysis