Takashi Toriu

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.

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%.

Detection of Tire-Road Contact Point for Vehicle Position Estimate Considering Shape Distortion in a Circular Fisheye Image

In this paper, we propose a new method for detecting tire-road contact points and estimating the position of a vehicle on a road taking the distortion in a circular fisheye image into consideration. In our proposed method, we use the distortional parameter of two concentric circles composed of the inner wheel and the outer tire by considering projection system of fisheye lens, and the searching the gray scale profile in the distortional direction are derived from each pixel that is outer the outline of the wheel region. And it is confirmed that the proposed method is effective in circular fisheye image by experimental results.

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.

Background modeling using special type of Markov Chain

Background modeling is important in video surveillance for extracting foreground regions from a complex environment. In this paper, we present a novel background modeling technique based on a special type of Markov Chain. The method is a substantial extension to the existing background subtraction techniques. First, a background pixel is statistically modeled by a linear regressive Gamma Markov distribution. Then, these statistical estimates are used as important parameters in background update schemes. The experimental results show that the proposed model is less sensitive to movements of the texture background and more robust for real time segmenting the foreground object accurately.

A Stochastic Model for Measuring Popularity and Reliability in Social Network Systems

Popularity and reliability information are crucial ingredients of today social networking systems such as Facebook, Linked In, YouTube, Twitter, and so on. In this paper, we propose a stochastic model for measuring and ranking popularity and reliability information in social networks. Specifically, by using the relationships between co-occurring users, we model a Markov chain for reliability measures and a queuing model for popularity measures based on time, space and scenarios. We then form a convex combination or fusion of the two measures to compute the Integrated Global Rank for social networks systems. Finally, we present some illustrative simulation results by using the social networks data collected from Twitter and YouTube. Experimental study indicates the effectiveness of proposed ranking algorithm in terms of better search results.

A Big Data application framework for consumer behavior analysis

More than ever before, the amount of data about consumers, suppliers and products has been exploding in today consumer world referred as “Big Data”. In addition, more data is available to the consumer world from multiple sources including social network platforms. In order to deal with such amount of data, a new emerging technology “Big Data Analytics” is explored and employed for analyzing consumer behaviors and searching their information needs. Specifically, this paper proposes a Big Data application framework for analyzing consumer behaviors by using topological data structure, co-occurrence methodology and Markov chain theory. First, the consumer related data is translated into a topological data structure. Second, using topological relationships, a co-occurrence matrix is formed to deduce Markov chain model for consumer behavior analysis. Finally, some simulation results are shown to confirm the effectiveness of the proposed framework.