International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022
p-ISSN: 2395-0072
www.irjet.net
Crime Detection using Machine Learning Christina Grace Nandigam, Nayana Ganesh Joshi, Swaranjali Bichukale, Vinisha Gomare Guide: Prof. R. H. Bhole Department of Information Technology, Zeal College of Engineering and Research, Affiliated with Savitribai Phule Pune University, Narhe, Pune-411041, Maharashtra, India ---------------------------------------------------------------------***-------------------------------------------------------------------
Abstract - Criminal Activity detection involves studying the body part or joint locations of a person from an image or a video. This project will involve the tracking of dubious human activity from live feeds of video surveillance by implementing CNN. Human Activity Analysis is an important issue that has been researched for years. It is necessary because of the unmitigated number of applications that can benefit from such tracking. For instance, human posture analysis can be used in applications that include animal behaviour understanding, surveillance using videos, sign language detection, and progressive human-computer interaction. Depth sensors have various drawbacks; they are limited to sedentary use, have very low resolution, contain noisy depth information, etc. These drawbacks make it difficult to estimate human poses from depth pictures. Neural networks can be used to overcome such problems. An active field in image processing research is human activity tracking and analysis from ocular observation. Through ocular observation, human actions can be supervised in jampacked areas like stations, banks, malls, airports, roads, schools, colleges, parking lots, etc. to thwart dubious, criminal actions such as robbery, mob activity, and nonlegal parking, violence, and other suspicious activities. It is futile to monitor such areas continuously, therefore intelligent ocular observation is necessary which can monitor human activities live and group them as normal and suspicious actions, and can trigger an alert.
varies from gaming to AR/VR, to healthcare and gesture recognition. In comparison to the domain of image data processing, there is very little amount of work on using CNNs for video analysis. It is because videos are more complex compared to images since they have another dimension to them — temporal. Unsupervised learning exploits temporal dependencies between frames and has proven successful for video analysis. Some human activity analysis programs use central processing units instead of graphical processing init so that the software can run on affordable hardware like mobile phones and embedded systems. Easily affordable sensors that can analyse the depth are another sort of technology in computational foresight. They are present in gaming consoles like Move for PlayStation. These motion sensors detect motion by simple hand gestures and do not need game controllers. They use structured light technology to access depth information. The depth values are inferred by the structured light sensors by the projection of an infrared light pattern onto a scene and analyzing the bending of the projected light pattern. These sensors, however, cannot be used on a large scale, and noisy depth information and low resolution render them incapable to analyze human postures from depth images.
2. PROBLEM STATEMENT One of today’s biggest problems is the manual analysis of readily available information credited to today’s technological advances. CCTVs, drones, satellite data, wearables, etc, provide a large amount of diverse data, and extracting strategic knowledge manually from this data is becoming more a problem than a solution. Automatic solutions are a critical necessity. This problem requires immediate solutions and this project will be the base for it by detecting suspicious/dubious activity from live feeds of CCTVs.
Key Words: CNN, Machine Learning, pre-processing, Classification, deep learning.
1. INTRODUCTION The plan is to build an application for the detection of dubious activity among people in areas of public interest places in real-time. Through ocular observation, human actions can be supervised in jam-packed areas like stations, banks, malls, airports, roads, schools, colleges, parking lots, etc. to thwart dubious, criminal actions such as robbery, mob activity, and non-legal parking, violence, and other suspicious activities. Deep learning and neural networks are going to be used to train the datasets in this system. This model will then be implemented as userfriendly software which will take the live feed from video surveillance as input and trigger an alert on the user’s device if some dubious activity is found. Human activity analysis is related to identifying human body parts and possibly tracking their movements. Real-life software of it
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3. LITERATURE SURVEY Bogden Ionescu, Razvan Roman, Marian Ghenescu, Marian Buric, and Florin Rastoceanu’s paper Artificial Intelligence Fights Crime and Terrorism at a New Level showed Artificial Intelligence (AI) as a new angle for delivering results with a human-grade precision. This paper served as the base paper for this project, providing various models and aspects to study and research. The only
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