Intelligent video surveillance

In the focus of this thesis are the new and modified algorithms for object detection, recognition and tracking within the context of video analytics. The manual video surveillance has been proven to have low effectiveness and, at the same time, high expense because of the need in manual labour of operators, which are additionally prone to erroneous decisions. Along with increase of the number of surveillance cameras, there is a strong need to push for automatisation of the video analytics. The benefits of this approach can be found both in military and civilian applications. For military applications, it can help in localisation and tracking of objects of interest. For civilian applications, the similar object localisation procedures can make the criminal investigations more effective, extracting the meaningful data from the massive video footage. Recently, the wide accessibility of consumer unmanned aerial vehicles has become a new threat as even the simplest and cheapest airborne vessels can carry some cargo that means they can be upgraded to a serious weapon. Additionally they can be used for spying that imposes a threat to a private life. The autonomous car driving systems are now impossible without applying machine vision methods. The industrial applications require automatic quality control, including non-destructive methods and particularly methods based on the video analysis. All these applications give a strong evidence in a practical need in machine vision algorithms for object detection, tracking and classification and gave a reason for writing this thesis. The contributions to knowledge of the thesis consist of two main parts: video tracking and object detection and recognition, unified by the common idea of its applicability to video analytics problems. The novel algorithms for object detection and tracking, described in this thesis, are unsupervised and have only a small number of parameters. The approach is based on rigid motion segmentation by Bayesian filtering. The Bayesian filter, which was proposed specially for this method and contributes to its novelty, is formulated as a generic approach, and then applied to the video analytics problems. The method is augmented with optional object coordinate estimation using plain two-dimensional terrain assumption which gives a basis for the algorithm usage inside larger sensor data fusion models. The proposed approach for object detection and classification is based on the evolving systems concept and the new Typicality-Eccentricity Data Analytics (TEDA) framework. The methods are capable of solving classical problems of data mining: clustering, classification, and regression. The methods are proposed in a domain-independent way and are capable of addressing shift and drift of the data streams. Examples are given for the clustering and classification of the imagery data. For all the developed algorithms, the experiments have shown sustainable results on the testing data. The practical applications of the proposed algorithms are carefully examined and tested

Introduction to intelligent video surveillance system

Most surveillance systems today provide only a passive form of site monitoring. Extensive video records may be kept to help find the instigator of criminal activities after the crime has been committed but preventive measures usually require human involvement. In addition to this, there is a need for large amounts of data storage to keep up to several terabytes of video streams that may be needed for later analysis. For any sense of real-time monitoring, guards often need to be employed to watch video feeds for hours on end to recognize suspicious, dangerous or potentially harmful situations. In multi-camera scene monitoring systems, this becomes quite infeasible as there can be up to 20 to 50 cameras on average in a large complex such as an airport or Megamall. However, monitoring and storage space are not the only concerns. Even if these costs can be borne, there is the additional problem of reviewing this vast amount of video data after a crime or incident has occurre

Particle-Filter-Based Intelligent Video Surveillance System

In this study, an intelligent video surveillance (IVS) system is designed based on the particle filter. The designed IVS system can gather the information of the number of persons in the area and hot spots of the area. At first, the Gaussian mixture background model is utilized to detect moving objects by background subtraction. The moving object appearing in the margin of the video frame is considered as a new person. Then, a new particle filter is assigned to track the new person when it is detected. A particle filter is canceled when the corresponding tracked person leaves the video frame. Moreover, the Kalman filter is utilized to estimate the position of the person when the person is occluded. Information of the number of persons in the area and hot spots is gathered by tracking persons in the video frame. Finally, a user interface is designed to feedback the gathered information to users of the IVS system. By applying the proposed IVS system, the load of security guards can be reduced. Moreover, by hot spot analysis, the business operator can understand customer habits to plan the traffic flow and adjust the product placement for improving customer experience

INTELLIGENT VIDEO SURVEILLANCE OF HUMAN MOTION: ANOMALY DETECTION

Intelligent video surveillance is a system that can highlight extraction and video summarization that require recognition of the activities occurring in the video without any human supervision. Surveillance systems are extremely helpful to guard or protect you from any dangerous condition. In this project, we propose a system that can track and detect abnormal behavior in indoor environment. By concentrating on inside house enviromnent, we want to detect any abnormal behavior between adult and toddler to avoid abusing to happen. In general, the frameworks of a video surveillance system include the following stages: background estimator, segmentation, detection, tracking, behavior understanding and description. We use training behavior profile to collect the description and generate statistically behavior to perform anomaly detection later. We begin with modeling the simplest actions like: stomping, slapping, kicking, pointed sharp or blunt object that do not require sophisticated modeling. A method to model actions with more complex dynamic are then discussed. The results of the system manage to track adult figure, toddler figure and harm object as third subject. With this system, it can bring attention of human personnel security. For future work, we recommend to continue design methods for higher level representation of complex activities to do the matching anomaly detection with real-time video surveillance. We also propose the system to embed with hardware solution for triggered the matching detection as output

Metadata extraction and organization for intelligent video surveillance system

Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA), 2010, p. 489-494The research for metadata extraction originates from the intelligent video surveillance system, which is widely used in outdoor and indoor environment for the aims of traffic monitor, security guard, and intelligent robot. Various features are extracted from the surveillance image sequences such as target detection, target tracking, object's shape and activities. However, the trend of more and more features being used and shared in video surveillance system calls for more attention to bridge the gap between specific analysis algorithms and enduser's expectation. This paper proposes a three-layer object oriented model to extract the surveillance metadata including shape, motion speed, and trajectory of the object emerging in image sequence. Meanwhile, the high-level semantic metadata including entry/exit point, object duration time is organized and stored which are provided for the further end-user queries. The paper also presents the experiment results in different indoor and outdoor surveillance scenarios. At last, a comparative analysis with another traditional method is presented. © 2010 IEEE.published_or_final_versio

Prediction of abnormal behaviors for intelligent video surveillance systems

IEEE Copyright Policies This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.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.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/17259/2004

Advance Intelligent Video Surveillance System (AIVSS): A Future Aspect

Over the last few decades, remarkable infrastructure growths have been noticed in security-related issues throughout the world. So, with increased demand for Security, Video-based Surveillance has become an important area for the research. An Intelligent Video Surveillance system basically censored the performance, happenings, or changing information usually in terms of human beings, vehicles or any other objects from a distance by means of some electronic equipment (usually digital camera). The scopes like prevention, detection, and intervention which have led to the development of real and consistent video surveillance systems are capable of intelligent video processing competencies. In broad terms, advanced video-based surveillance could be described as an intelligent video processing technique designed to assist security personnel’s by providing reliable real-time alerts and to support efficient video analysis for forensic investigations. This chapter deals with the various requirements for designing a robust and reliable video surveillance system. Also, it is discussed the different types of cameras required in different environmental conditions such as indoor and outdoor surveillance. Different modeling schemes are required for designing of efficient surveillance system under various illumination conditions

Person Re-Identification Techniques for Intelligent Video Surveillance Systems

Nowadays, intelligent video-surveillance is one of the most active research fields in com- puter vision and machine learning techniques which provides useful tools for surveillance operators and forensic video investigators. Person re-identification is among these tools; it consists of recognizing whether an individual has already been observed over a network of cameras. This tool can also be employed in various possible applications, e.g., off-line retrieval of all the video-sequences showing an individual of interest whose image is given as query, or on-line pedestrian tracking over multiple cameras. For the off-line retrieval applications, one of the goals of person re-identification systems is to support video surveillance operators and forensic investigators to find an individual of interest in videos acquired by a network of non-overlapping cameras. This is attained by sorting images of previously ob- served individuals for decreasing values of their similarity with a given probe individual. This task is typically achieved by exploiting the clothing appearance, in which a classical biometric methods like the face recognition is impeded to be practical in real-world video surveillance scenarios, because of low-quality of acquired images. Existing clothing appearance descriptors, together with their similarity measures, are mostly aimed at im- proving ranking quality. These methods usually are employed as part-based body model in order to extract image signature that might be independently treated in different body parts (e.g. torso and legs). Whereas, it is a must that a re-identification model to be robust and discriminate on individual of interest recognition, the issue of the processing time might also be crucial in terms of tackling this task in real-world scenarios. This issue can be also seen from two different point of views such as processing time to construct a model (aka descriptor generation); which usually can be done off-line, and processing time to find the correct individual from bunch of acquired video frames (aka descriptor matching); which is the real-time procedure of the re-identification systems. This thesis addresses the issue of processing time for descriptor matching, instead of im- proving ranking quality, which is also relevant in practical applications involving interaction with human operators. It will be shown how a trade-off between processing time and rank- ing quality, for any given descriptor, can be achieved through a multi-stage ranking approach inspired by multi-stage approaches to classification problems presented in pattern recogni- tion area, which it is further adapting to the re-identification task as a ranking problem. A discussion of design criteria is therefore presented as so-called multi-stage re-identification systems, and evaluation of the proposed approach carry out on three benchmark data sets, using four state-of-the-art descriptors. Additionally, by concerning to the issue of processing time, typical dimensional reduction methods are studied in terms of reducing the processing time of a descriptor where a high-dimensional feature space is generated by a specific person re-identification descriptor. An empirically experimental result is also presented in this case, and three well-known feature reduction methods are applied them on two state-of-the-art descriptors on two benchmark data sets

Algorithms for people re-identification from RGB-D videos exploiting skeletal information

In this thesis a novel methodology to face people re-identification problem is proposed. Re-identification is a complex research topic representing a fundamental issue especially for intelligent video surveillance applications. Its goal is to determine the occurrences of the same person in different video sequences or images, usually by choosing from a high number of candidates within a datasetope