A study on surveillance video abstraction techniques

The goal of surveillance video abstraction is to generate a video abstract that includes important events and object by eliminating the redundant frames, lacking from activity in original video. Although many research and progresses have been done in video abstraction, the developed approaches either fail to accurately and effectively cover the overall visual content of video or they are computationally expensive in term of time or process. In this paper, firstly we critically review the applicable video abstraction techniques in surveillance domain based on our hierarchical classification, and then briefly introduce a new approach for generating a static surveillance video abstraction, which mitigate the drawbacks of reviewed approaches

Object detection and representation method for surveillance video indexing

The huge volume of videos produced by surveillance cameras has increased the demand for the fast and effective video surveillance indexing and retrieval systems. Although environmental condition such as light reflection, illumination changes, shadow, and occlusion can affect the indexing and retrieval result of any video surveillance system, nevertheless the use of reliable and robust object (blob) detection and representation methods can improve the performance of the system. This paper presents a video indexing module, which is part of a video surveillance indexing and retrieval framework, to overcome the above challenges. The proposed video indexing module is composed of seven components: background modeling, foreground extraction, blob detection, blob analysis, feature extraction, blob representation, and blob indexing. The experimental results showed that the selection of appropriate blob detection method could improve the performance of the system. Moreover, the experiments also demonstrated that the functionality of the proposed blob representation method was able to prevent the processing of redundant blobs' information

Speeded up surveillance video indexing and retrieval using abstraction

Many researches have been conducted on video abstraction for quick viewing of video archives, however there is a lack of approach that considers abstraction as a pre-processing stage in video analysis. This paper aims to investigate the efficiency of integrating video abstraction in surveillance video indexing and retrieval framework. The basic idea is to reduce the computational complexity and cost of overall processes by using the abstract version of the original video that excludes unnecessary and redundant information. The experimental results show a significant reduction of 87% in computational cost by using the abstract video rather than the original video in both indexing and retrieval processes

A review of current trend on data management and quality in data communication

Data communication is one of the areas that can be explored by researchers in the current digital world. The aspects of data communication could be divided into several categories such as the acquisition, transmission, processing, storage, etc. This paper reviews some of the previously written papers related to enhancement in data communication technology published between 2007 and 2012 in IEEE Explorer, Science Publications and ACM. However, only 33 of the above papers are considered for this review. In this paper, data communication aspects are divided into three categories namely (a) data processing, (b) data transmission and (c) data monitoring and control. Based on the review, the current data management requires fast and quick processing, transmission and retrieval. This is due the the change of paradigm from centralized to mobile data storage and retrieval and also the volume of data that is always increasing that requires better and fast result for organizations. This paper presents the current trend of data management and quality in data communication. The challenge posed by current data communication advancement through emerging mobile device and application become new research opportunities for researchers to create or to improve current method of data processing and retrieval

Crowd behavior classification based on generic descriptors

Crowd behavior analysis plays an important role in high security interests in public areas such as railway stations, shopping centres, and airports, where large populations gather. The crowded scenes vary in various densities, structures and occlusion. It brings enormous challenges in identifying generic descriptors to describe motion dynamics caused by pedestrians walk in different directions with extremely diverse behaviors. Therefore, this research is proposal an approach for crowd behavior analysis to recognize the common properties across different crowded scenes. The recognized common properties are then used to identify generic descriptors from group-level for crowd behavior classification

A customized non-exclusive clustering algorithm for news recommendation systems

Clustering is one of the main tasks in machine learning and data mining and is being utilized in many applications including news recommendation systems. In this paper, we propose a new non-exclusive clustering algorithm named Ordered Clustering (OC) with the aim is to increase the accuracy of news recommendation for online users. The basis of OC is a new initialization technique that groups news items into clusters based on the highest similarities between news items to accommodate news nature in which a news item can belong to different categories. Hence, in OC, multiple memberships in clusters are allowed. An experiment is carried out using a real dataset which is collected from the news websites. The experimental results demonstrated that the OC outperforms the k-means algorithm with respect to Precision, Recall, and F1-Score

HYPNER: a hybrid approach for personalised news recommendation

A personalised news recommendation system extracts news set from multiple press releases and presents the recommended news to the user. In an effort to build a better recommender system with high accuracy, this paper proposes a personalised news recommendation framework named Hybrid Personalised NEws Recommendation (HYPNER). HYPNER combines both collaborative filtering-based and content-based filtering methods. The proposed framework aims at improving the accuracy of news recommendation by resolving the issues of scalability due to large news corpus, enriching the user's profile, representing the exact properties and characteristics of news items, and recommending diverse set of news items. Validation experiments showed that HYPNER achieved 81.56% improvement in F1 -score and 5.33% in diversity as compared to an existing recommender system, SCENE

Measuring computer security awareness on internet banking and shopping for internet users

Internet banking and shopping are two main e-commerce activities that are popular among Internet users. However, millions of dollars have lost due to the compromised bank account of users that have been hacked or intercept by irresponsible person through the Internet. The increase of threats towards online banking and shopping has made the study towards the awareness on internet banking and shopping to be important.In this study, a group of respondents with different academic background, age and gender responded to a survey that questions about their awareness of utilizing internet banking and shopping services. The results are then examined and analyzed by dividing them into group based on gender and education background.The results also analyzed based on the category of the questions related to basic and technical awareness towards the proper usage of internet banking and shopping. The results of the study showed majority of the users have good awareness especially on the basic Internet security steps taken while respondents with lower academic background lack technical awareness on Internet shopping and banking

Discovering dependencies among data quality dimensions : a validation of instrument.

Improving data quality is a basic step for all companies and organizations as it leads to increase opportunity to achieve top services. The aim of this study was to validate and adapt the four major data quality dimensions’ instruments in different information systems. The four important quality dimensions which were used in this study were; accuracy, completeness, consistency and timeliness. The questionnaire was developed, validated and used for collecting data on the different information system’s users. A set of questionnaire was conducted to 50 respondents who using different information systems. Inferential statistics and descriptive analysis were employed to measure and validate the factor contributing to quality improvement process. This study has been compared with related parts of previous studies; and showed that the instrument is valid to measure quality dimensions and improvement process. The content validity, reliability and factor analysis were applied on 24 items to compute the results. The results showed that the instrument is considered to be reliable and validate. The results also suggest that the instrument can be used as a basic foundation to implicate data quality for organizations manager to design improvement process

A web-based interrogative ontology retrieval application for unstructured documents

This paper presents an implementation of Retrieval Interrogative Ontology Analysis application based on the MalayIK-Ontology approach. This application enables querying in simple ways, while increasing human understanding of unstructured document in Malay language. To evaluate the application, a survey is conducted to measure the level of satisfaction of the application by 30 participants. Results from the questionnaires analysis show that the interrogative contextual information is able to facilitate human understanding of unstructured documents as it is supported by additional information annotation. The analysis also concludes that the Malay knowledge representation by concepts is able to generalize the Malay common language as knowledge representation