Learning and Classification of Events in Monitored Environments

2012

This paper presents a survey of Intelligent Visual Surveillance Systems (IVSS) with the aim to identify the needs and requirements of future large-scale (IVSS). Pros and cons of existing IVSS are discussed from different perspectives including: motion segmentation, region of interest (ROI) characterization, region of interest tracking, event recognition, and scenario profiling. Most IVSS approaches have limited fusion focus, superficial, and lack sufficient cohesiveness and completeness. By introducing sensor data and information fusion techniques, on the other hand, one can significantly improve the critical decision making at different levels of information abstraction and hence reducing decision uncertainty and improving ROI profiling in context-sensitive environments. In this paper, we have systematically addressed technological needs of different approaches supporting IVSS and identified a future direction to further enhance the current state-of-the-art of IVSS for homeland sec...

NATO SCIENCE SERIES SUB SERIES …, 2005

The last stage of any type of automatic surveillance system is the interpretation of the acquired information from the sensors. This work focuses on the interpretation of motion pictures taken from a surveillance camera, i.e.; image understanding. An expert system is presented which can describe in a natural language like, simple human activity in the field of view of a surveillance camera. The system has three different components: a pre-processing module for image segmentation and feature extraction, an object identification expert system (static model), and an action identification expert system (dynamic temporal model). The system was tested on a video segment of a pedestrian passageway taken by a surveillance camera.

ACM Computing Surveys, 2022

CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety...

Discover Internet of Things

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support Sy...

Video security is becoming more and more vital today due to rapid development of hardware equipments as the number of installed cameras can confirm. This paper presents the system architecture of IVSS, an Intelligent Video Surveillance System based on IPcameras and deployed in an academic environment. In fact, Video surveillance is increasingly found in academic institutions. It is used to oversee the safety of teachers and students, as well as to protect assets from vandalism and theft. In this work the surveillance system is deployed in our university environment, it is based on a set of digital IP video cameras linked by the university IP network infrastructure. Our system architecture is based on efficient moving object detecting and tracking algorithm and a robust statistical activity recognition framework based on SVM which is used for modeling activities. The experimental results on real-time video streams show the feasibility of our system and its effectiveness in human activity recognition.

With increased technology and population, surveillance is becoming key area in research. The best utilization of technology for the surveillance is the focus area today. The focus of the project would be to automate the detection of actions performed in front of camera. Monitoring the events on camera manually will not be possible. Even if the event had happened in the past, searching the same event in the video manually would cost a lot time. There is scope for automation which should detect those events and provide detailed textual result which can be processed easily. This project also considers effective way to store the video surveillance data consuming as small memory as possible along with smart index information. Video surveillance is an important security asset to control theft, traffic monitoring, banks, department stores, highways, crowded public places and borders.

Computational Intelligence and …, 2007

The OBSERVER is a video surveillance system that detects and predicts abnormal behaviors aiming at the intelligent surveillance concept. The system acquires color images from a stationary video camera and applies state of the art algorithms to segment, track and classify moving objects. In this paper we present the behavior analysis module of the system. A novel method, called Dynamic Oriented Graph (DOG) is used to detect and predict abnormal behaviors, using real-time unsupervised learning. The DOG method characterizes observed actions by means of a structure of unidirectional connected nodes, each one defining a region in the hyperspace of attributes measured from the observed moving objects and having assigned a probability to generate an abnormal behavior. An experimental evaluation with synthetic data was held, where the DOG method outperforms the previously used N-ary Trees classifier.

2021

The significance of security in day to day life is increasing, and hence the use of a video surveillance system is becoming commonly accepted in almost every public places. Even though by placing surveillance system in public places will help to trace out the culprits of the anomalous situation, it is hard to trace it out. The knowledge of the incidence time is needed to identify the event, and also, it requires an enormous data handling task. To identify the anomalous situation in real-time will help the authorities to decrease the consequences and loss during the anomalous event. The paper proposes an in-depth study of various automatic anomaly detection techniques which helps to reduce the loss occurred of the anomalous situation. The advancements in Artificial Intelligence help in the quick and automatic identification of nominal and anomalous events. The sequential and incremental learning approach in feature extraction will help to generate a model that will provide much accur...

IEEE Winter Conference on Applications of Computer Vision, 2014

Computer Vision problems applied to visual surveillance have been studied for several years aiming at finding accurate and efficient solutions, required to allow the execution of surveillance systems in real environments. The main goal of such systems is to analyze the scene focusing on the detection and recognition of suspicious activities performed by humans in the scene, so that the security personnel can pay closer attention to these preselected activities.