A Cooperative Framework for Integrating Ontologies in a Networked Surveillance System

Soomi Yang Department of Information Engineering

The University of Suwon Gyeonggi-do, Korea

smyang@suwon.ac.kr

Abstract—In a networked surveillance system distributed ontology construction processes are required for the better inference. To make use of a cooperative framework for integrating ontologies, we describe a computing framework for the integration of ontologies supplied by a predetermined set of data source servers, who may be interconnected through a peer to peer network. In our approach, every data source server agent consults the derived ontology constructed by the neighbors in order to refine its private local ontology. In addition, the model proposed in this paper allows the agents involved in the construction of the distributed ontology to use their own recognition technology. Common APIs are supplied for querying the global ontology. For such a P2P network, an effective cache framework that can handle heterogeneous devices is provided.

Keywords-component; Networked Surveillance Systems, Multimedia Data, Biometric Data. Peer to Peer Computing

I. INTRODUCTION Context inference including distributed multimedia data

with biometric is widely used in distributed surveillance environment. In such a distributed surveillance environment, surveillance devices such as smart cameras may carry heterogeneous video data with different transmission ranges, latency, and formats. These devices not only can get services from a region server, but also they can form and generate a P2P(peer-to-peer) network to provide services to each other. For such a P2P network, an effective cache framework that can handle huge multimedia data is required. In this paper, we propose a flexible cache scheme which is adaptive to the actual device demands and that of its neighbors. Our scheme shows the efficiency of caching resulted in better context inference.

The popularity of smart cameras providing many intelligent functions grows with the advance of electronic technologies and Internet applications. In the upcoming era, many applications such as data sharing, intelligent biometric techniques and distributed web services for context reasoning will be widely used in the distributed surveillance systems. On the other hand, P2P systems have now been widely used in lots of applications on the Internet. Structured P2P systems provide both low latency and desired load balance. In our network surveillance environment, each

surveillance devices is equipped with an agent utilizing P2P systems.

The distribution of demands for multimedia data items is often skewed, and the surveillance devices have different capabilities and data formats. These can lead to poor data communication and dropped messages. In this paper, we propose a distributed adaptive cache scheme based on heterogeneous device and data network. Our scheme uses caching and conformity to update and share data in a cooperative way. Simulation studies are conducted to evaluate the effectiveness of our flexible cache scheme. Implementation is also going on into our distributed surveillance network environment.

The structure of the paper is as follows, Section 2 surveys related work of cooperative integration of ontologies schemes. In Section 3, ontology in networked surveillance is explained. Section 4 describes the cooperative peer to peer computing through common APIs. In Section 5, simulation results are presented and the performance is evaluated. We also show our implementation results. Finally, Section 6 concludes with an outline of our future work.

II. RELATED WORK There has been several cooperative ontology integration

schemes proposed with analogical peers. However in a networked surveillance, various surveillance devices with heterogeneous techniques and generating diverse biometric data are coexist. Numerous studies have been carried out on the caching and replication techniques in distributed environments. Especially [1] dealt with multimedia data caching technique. In [2], a cooperative caching framework is introduced and claimed to be effective to data availability. In [3], a replica allocation method and clustering in distributed networks are introduced to improve data accessibility in a mobile communication environment. Another cooperative cache scheme for similar peers is described in [4], which combine the P2P communication technology with a conventional mobile system. However, previous works were mostly based on the ground that the distributed nodes in the network have the same characteristics, and the data transmitted is standardized and intermittent. Our distributed surveillance environment, multimedia data and biometrics data are generated continuously and analysis should be done in real time.

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In this paper, we adopted several approaches of previous works. To address those challenges, we advocate flexible peer to peer communication and an adaptive caching framework in which heterogeneous devices with different transmission ranges, latency and even cache sizes co-exist in the distributed surveillance network.

III. ONTOLOGY IN NETWORKED SURVEILLANCE Cooperative inference such as object tracking requires

the cooperation among local systems[5]. Data associations between data sources are required. The availability of small, power-aware, and high-performance camera nodes or sensor nodes can be combined into hierarchical surveillance networks in several levels. For more efficient collaboration, we make the infrastructure of the regional surveillance networks hierarchical tree structure by regional and administrative levels.

Our project is aimed at designing and deploying a scalable system for the surveillance of province area containing about 15 cities. Final product is to be included in the construction of the ubiquitous cities. We should consider integrated framework of various networked devices, such as CCTVs, RFID sensors, sound monitors, smoke detectors, smart cameras, etc. The agent platform is distributed across different data acquisition systems. The data source server can be a single camera node or sensor node. Possibly it can be a back end server for several cameras or sensor nodes. A terse scenario for some subject tracking between the two data source servers is explained in Figure 1. To help the understanding and easy explanation, the agents are detached from devices.

The agents can exist in any level. In a hierarchical surveillance network, the lowest level regional server is called ‘level 1 server’. Level 1 region servers receive data from data source servers in their domain and manage them. Rarely upper level servers can have data source server of their own.

In our approach, several agents are encouraged to work on an ontology construction process in a cooperative way. Data source servers form a graph structure. They can communicate each other freely within access control permission to perform their own intelligent distributed inference based on their own ontology knowledge base[6] which adopted globally acquired ontology data.

IV. COOPERATIVE PEER TO PEER COMPUTING To construct large scale ontologies, for each data request,

data source server ni first tries to find the required data item from its local cache. If it encounters a local cache miss, it broadcasts a request message to its neighbor peers via P2P broadcast communication. If a neighbor peer has the required data item, it sends a reply message to ni via P2P communication. However, if ni does not get any reply from the network after the time out period, it will instead send the the request to the higher region server to obtain the required data.

For the broadcasting, peers can exchange such information as the biometric data formats, and the transmission methods. They can negotiate and come up with

a communication strategy based on the messages exchanged. For the negotiation and data exchange we provide several common APIs based on the standard issued by ONVIF[7]

As explained in Section 3, the multimedia data and biometric data caching will be distributed over the region servers and data source servers. The data source server will need to carry out the indexing and retrieval of the information distributed across the servers in an efficient manner. To aid the task, we could use a data structure containing only the information related to the cached objects. As a result, better usage of their limited cache space and higher system performance can be obtained.

In a distributed surveillance environment, multimedia data and biometric data are produced continuously. To maintain the freshness and effectiveness of the data, we should cache data adaptively. To combine these approaches, we propose a framework for supporting cooperative ontology construction base on the importance of data. We use the term ‘weight’ of cached data to describe its relative importance as compared to the other data as proposed in [1]. The higher the weight, the lower is the probability of the data being replaced.

In such a situation other issue is the maintenance of consistency among inter-dependent ontologies because each ontology is revised asynchronously by different agents. Therefore data replacement policy with efficient use of their limited cache space is the key factor of the performance of networked surveillance.

To evaluate the performance we measure the average expected packet transmission which can be computed as follows,

)1()1

1()1()(h

wp

LqTE ⋅⋅−

⋅⋅−= (1)

where E(T) means the expectation of the packet traffic, q means the probability of having data cached, L represents the latency, p means the probability of packet loss, w is the weight of data, and h is the number of hops.

V. SIMULATION AND IMPLEMENTATION To evaluate the performance of our system, we simulated

several specific cases and implemented for small area, our university. For each given caching ratio q, we can inspect the effects to the average packet transmission. Figure 2 shows the expected packet transmission by packet loss p and caching ratio q. When the packet loss is small, the difference is small. However, as the packet loss gets bigger, it suffers more increasing packet transmission. For the difference of the cases, the ‘cached’ case is when the adaptive caching technique is applied and the ‘uncached’ case is when the caching is not applied. In both cases, the data source servers are grouped by region. Therefore the caching is skewed by region.

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Figure 2. A comparison of the expected packet transmission by caching

We implemented our cooperative framework for integrating ontologies with an common API, adaptive caching technique into our distributed surveillance system. Figure 3 and 4 shows several user interfaces. Web-based user interface shown in Figure 3 lists biometric data recognized on the right frame. On the central frame includes google map and marks for peculiar events. The event lists on the left frame are the results of the integrated inference. When we click each of the event items, it pops up related information. The subject tracking through the communication between the independent data source servers is possible. They request and inquire data for a combined inference through common API on the peer to peer computing framework. In Figure 4, we show touch user interface using smart phone and operator interface console which is linked to the web user interface. Touch user interface is going to be updated to show the events list and biometric data.

Figure 3. Web-based user interface

Figure 4. CCTV camera control interfaces

VI. CONCLUSION We describe the cooperative surveillance guidelines for

better context inference based on distributed ontology framework. Data source servers and regional servers communicate each other freely within access control permission to perform their own intelligent distributed inference based on their own ontology knowledge base. Peer to peer computing with common API supports the flexible and scalable communication framework.

Context inference including distributed multimedia data and biometric data is widely used in distributed surveillance environment. For a distributed surveillance environment, surveillance devices such as smart cameras may carry heterogeneous video data with different transmission ranges, latency, and formats. These devices not only can get services from a region server, but also they can form and generate a peer to peer network to provide services to each other. For such a peer to peer network, an effective cache framework that can handle heterogeneous devices is required. In this paper, we propose a flexible cache scheme which is adaptive to the actual device demands and that of its neighbors. Our scheme uses caching and conformity to update and share data in a cooperative way. Our scheme shows the efficiency of integrating ontologies resulted in better context inference.

Simulation and implementation are conducted to evaluate the effectiveness of our flexible cooperative framework for integrating ontologies. Final product of our implementation will be implanted in the environment of the ubiquitous city. More realistic data will be given in future work.

ACKNOWLEDGMENT This work was supported by the GRRC program of

Gyeonggi province. [GGA0801-45700, Center for U-city Security and Surveillance Technology]

REFERENCES [1] G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of

Paknikar, A., Kankanhalli, M., and Ramakrishnan, K., 2000. A Caching and Streaming Framework for Multimedia, ACM Multimedia

[2] L.Yin, G. Cao, 2006. Supporting Cooperative Caching in Ad Hoc Networks. IEEE Tr. On mobile computing, Vol. 5, No. 1.

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[3] Zheng, J., Su, J., and Lu, X. 2005. A Clustering based Data Replication Algorithm in Mobile Ad Hoc Networks for Improving Data Availability, Lecture Notes in Computer Science 3358

[4] Chow, C., Leong, H., and Chan, A. 2005. Distributed Group-based Cooperative Caching in a Mobile Broadcast Environment, MDM 2005

[5] A. Sankaranarayanan, A. Veeraraghavan, and R. Chellappa, 2008, Object Detection, Tracking and Recognition for Multiple Smart Camaras, Proceedings of the IEEE, vol. 96, no. 10

[6] J. Z. Pan, 2007, A Flexible Ontology Reasoning Architecture for the Semantic Web, IEEE Transactions on Knowledge and Data Engineering archive Volume 19 , Issue 2

[7] ONVIF, 2009, Open Network Video Interface Forum Core Specification version 1.01

Figure 1. Surveillance steps with a subject tracking

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