Thi Thi Zin

Unattended object intelligent analyzer for consumer video surveillance

Consumer video camera surveillance with the continuous advancements of image processing technologies is emerging for consumer world of applications. Technology for detecting objects left unattended in consumer world such as shopping malls, airports, railways stations has resulted in successful commercialization, worldwide sales and the winning of international awards. However, as a consumer video application the need is now greater than ever for a surveillance system that is robustly and effectively automated. In this paper, we propose an intelligent vision based analyzer for semantic analysis of objects left unattended relation with human behaviors from a monocular surveillance video, captured by a consumer camera through cluttered environments. Our analyzer employs visual cues to robustly and efficiently detect unattended objects which are usually considered as potential security breach in public safety from terrorist explosive attacks. The proposed system consists of three processing steps: (i) object extraction, involving a new background subtraction algorithm based on combination of periodic background models with shadow removal and quick lighting change adaptation,(ii) extracted objects classification as stationary or dynamic objects, and (iii) classified objects investigation by using running average about the static foreground masks to calculate a confidence score for the decision making about event (either unattended or very still person). We show attractive experimental results, highlighting the system efficiency and classification capability by using our real-time consumer video surveillance system for public safety application in big cities.

Fusion of Infrared and Visible Images for Robust Person Detection

In the current context of increased surveillance and security, more sophisticated and robust surveillance systems are needed. One idea relies on the use of pairs of video (visible spectrum) and thermal infrared (IR) cameras located around premises of interest. To automate the system, a robust person detection algorithm and the development of an efficient technique enabling the fusion of the information provided by the two sensors becomes necessary and these are described in this chapter. Recently, multi-sensor based image fusion system is a challenging task and fundamental to several modern day image processing applications, such as security systems, defence applications, and intelligent machines. Image fusion techniques have been actively investigated and have wide application in various fields. It is often a vital pre-processing procedure to many computer vision and image processing tasks which are dependent on the acquisition of imaging data via sensors, such as IR and visible. One such task is that of human detection. To detect humans with an artificial system is difficult for a number of reasons as shown in Figure 1 (Gavrila, 2001). The main challenge for a vision-based pedestrian detector is the high degree of variability with the human appearance due to articulated motion, body size, partial occlusion, inconsistent cloth texture, highly cluttered backgrounds and changing lighting conditions.

A Markov Random Walk Model for Loitering People Detection

Today video surveillance systems are widely used in public spaces, such as train stations or airports, to enhance security. In order to observe large and complex facilities a huge amount of cameras is required. These create a massive amount of data to be analyzed. It is therefore crucial to support human security staff with automatic surveillance applications, which will create an alert if security relevant events are detected. This way video surveillance could be used to prevent potentially dangerous situations, instead of just being used as forensic instrument, to analyze an event after it happened. In this treatise we present a surveillance system which supports human operators, by automatically detecting loitering people. Usually, loitering human behavior often leads to abnormal situations, like suspected drug-dealing activity, bank robbery, and pickpocket, etc. Thus, the problem of loitering detection in image sequences involving situations with multiple objects is studied based two dimensional Markov random walks in which both motion and appearance features describing the movements of a varying number of objects as well as their entries and exits are used. To obtain efficient and compact representations we encode the spatiotemporal information of intra-inter trajectory contexts into the transition matrix of a Markov Random Walk, and then extract its stationary distribution and boundary crossing probabilities as final detection criteria. The model is also made less sensitive to uninteresting objects occluding the region of interest by integration out their effect on the observation probabilities. The resulting system is tested on the real life dataset scenarios giving 95% performance results.

Robust road sign recognition using standard deviation

This paper proposes the recognition of road signs under various illumination conditions using standard deviation. The proposed road sign recognition system uses both of shape and color information. The former one is used in template matching with standard deviation and the latter one in modified Mahalanobis distance. This paper focuses on circular road signs and achieves almost perfect performances under various conditions such as daytime, evening-time and nighttime. Moreover, the proposed method may be applicable to recognize of any shape of road signs. The effectiveness of the proposed method has been demonstrated through experiments. According to our experimental results, the recognition rate is 100% for 200 images taken under various illumination conditions.

Dominant Color Embedded Markov Chain Model for Object Image Retrieval

By focusing on the decoding process to improve coding efficiency, we propose an effective video coding method by employing parameter estimation in the decoding process. A bitrate reduction can be achieved when the proposed method is applied to the DC transform coefficient and the motion vector of H.264. 0.25%-0.84%.

Optimal Crawling Strategies for Multimedia Search Engines

In this paper we propose a novel optimal crawling strategy for next-generation multimedia search engines. We consider here a Web crawl as a two-dimensional (2D) random walker on a graph whose vertices are the Web pages and whose edges are the hyperlinks. The proposed crawler is a two-part scheme optimizing the crawling process in such a way that the average level of staleness over all pages is minimized and the quality of search engine from user’s perspective is maximized. In doing so, we employ techniques from probability theory and the theory of functional equations which are highly computationally efficient-crucial for practicality because the size of the problem in the Web environment is immense. We show that a combination of breadth-depth crawling including the largest sites is a practical and optimal strategy. In particular, several probabilistic models for user browsing in infinite Web are proposed and studied to estimate how deep and breadth a crawler must go to download a significant portion of the Web site that is actually visited. Experimental and simulation results show that a crawler needs to download just a few levels in depth and breadth to reach the maximum number of pages that users actually visit. It also suggests that the largest sites should be included in the crawling process.

Optimal color space for relative color polygons

We aim for detection and recognition of planar objects in natural outdoor scenes under varying illumination conditions. To achieve this, we propose Relative Color Polygons (RCPs) using component colors of objects for color matching. They can be defined on many color spaces, and it is found that a 2D color space (XY space) is the optimal color space for our relative color method compared with other color spaces. To evaluate the invariance to illumination changes for object recognition, experiments have been carried out using 500 outdoor scene images. By using the proposed model, the color matching rate of the input images with the standard one was 95%. This framework is potentially applicable to image retrieval, image segmentation, image recognition, and so on.

Reliability and availability measures for Internet of Things consumer world perspectives

The Internet of Things in which things or objects are connected becomes important in modern society. It also reflects to our Consumer World in which objects or things such as cell phones, consumer products, smart homes, cars, TVs etc. are in the World Wide connections. One of the most challenging problems is concerned with the defining and computing of reliability and availability measures since an object or thing or device quality of service failures can lead to dangerous situations for people as well as physical infrastructures In this paper, we propose a probability based concept for measuring the reliability and availability of the devices and things connected in the Internet of Things. We will investigate the proposed model from the perspectives of consumer world by using things link analysis. For evaluation, some simulation results are presented.

An integrated framework for detecting suspicious behaviors in video surveillance

In this paper, we propose an integrated framework for detecting suspicious behaviors in video surveillance systems which are established in public places such as railway stations, airports, shopping malls and etc. Especially, people loitering in suspicion, unattended objects left behind and exchanging suspicious objects between persons are common security concerns in airports and other transit scenarios. These involve understanding scene/event, analyzing human movements, recognizing controllable objects, and observing the effect of the human movement on those objects. In the proposed framework, multiple background modeling technique, high level motion feature extraction method and embedded Markov chain models are integrated for detecting suspicious behaviors in real time video surveillance systems. Specifically, the proposed framework employs probability based multiple backgrounds modeling technique to detect moving objects. Then the velocity and distance measures are computed as the high level motion features of the interests. By using an integration of the computed features and the first passage time probabilities of the embedded Markov chain, the suspicious behaviors in video surveillance are analyzed for detecting loitering persons, objects left behind and human interactions such as fighting. The proposed framework has been tested by using standard public datasets and our own video surveillance scenarios.

An Integrated Framework for Disaster Event Analysis in Big Data Environments

Today world has witnessed the catastrophic consequences of natural and man-made disasters are demanding the urgent need for more research to advance fundamental knowledge and innovation for disaster prevention, mitigation and management. At the same time, the world is in the age of the Big Data revolution which holds the potential to mitigate the effects of disaster events by enabling access to critical real time information. Thus, in this paper an integrated framework for analyzing disaster events by using the Big Data analytics is proposed. The proposed framework shall address three key components to perform data organization, data integration and analysis, information presentation to users by utilizing Big Data with respect to disaster events. In doing so, the paper shall create a disaster domain-specific search engine using co-occurring theory and Markov chain concepts for preparing impacts of disaster attacks to make the society better aware of the situations. Specifically, stochastic clustering with constraints is used to automatically extract disaster events by defining the set of structural attributes. Some illustrative simulations are shown by using Big Data sources for the Great East Japan earthquake, tsunami and nuclear disaster events of 2011.