Analyzing Large-Scale News Video Databases to Support Knowledge Visualization and Intuitive Retrieval

In this paper, we present a schema for content-based analysis of broadcast news video. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at various levels of abstraction via effective integration of video, audio, and text data available from the news programs. Based on these news video structure and content analysis technologies, a TV news video Library is generated, from which users can retrieve definite news story according to their demands.

With the continuous development of video detection technology, the video analysis technology based on campus security has become an important part of the construction of safe campus. As the college students still are a group that has poor ability of security protection, campus security issue is closely related to the stability of society and family happiness, and has become a topic of concern to the whole society. The intelligent vision-based campus public safety monitoring system is an important means to achieve security monitoring, it can automatically analyze the video image sequence, and detect, track and identify objects in the monitoring scene without human intervention, and make high-level understanding and analysis of behaviors on this basis. Most of the existing visual monitoring systems can collect and store video data, and the real-time event detection task can automatically be generated through background analysis. Intelligent visual monitoring system should not only be used for accident investigation, but also be used to prevent potential disasters and accidents. The system is consisted of system management platform, event mining and analysis, monitoring and extraction of moving targets, forecasting and tracking targets. The paper makes an in-depth study on the application of intelligent visual detection technology on campus. Based on the intelligent visual video analysis, hidden Markov model is adopted in the paper for video event detection and analysis, motion features and shape features are taken as the observation data, and segmentation method is adopted to analyze the influence of video viewing height and angle on the detection result.

Nowadays, digital surveillance systems are ubiquitously installed and continuously generating huge amount of video data. More often than not, it requires human to identify potential threats. To address that problem, we propose a Platform-as-a-Service (PaaS) architecture to conduct large scale video analysis in more systematic and efficient ways. In addition, the video analysis application implemented on the proposed PaaS platform provides a more user friendly interface for analyzing video footage. In this paper, we present the construction and implementation steps of a real-world video analysis service based on our previous work for a citywide security service. We successfully integrated over 25 thousands of surveillance devices throughout the city. The integrations of the NVR/DVRs, license plate number recognition engine, and the camera health monitoring engine will be demonstrated. Finally, we conclude with discussions on why we believe the proposed solution has the potential to become the standard for deploying intelligent video surveillance network.

In this paper we propose temporal and optimized video scene segmentation techniques. Temporal video scene segmentation methods aims to partition the video to elementary image sequences termed scenes. The main purpose of video scene segmentation is to extract objects from series of consecutive video frames. One important goal in video analysis is to group the shots, such that all shots in a single scene are related to the same subject. In this paper we make an analysis on four methods of video scene segmentation. The methods proposed for segmentation is Backward shot coherence(BSC), Best first model merging(BFMM) and Markov chain Monte Carlo (MCMC) and Hierarchical chain partitioning(HCP). Video scene segmentation is performed by considering both content coherence and temporally contextual dissimilarity. This task is formulated as a ratio function of content coherence within a scene and contextual dissimilarity of adjacent scenes. Finally we propose an optimized video scene segmentation which is a novel approach that is used to segment the video scene and thereby optimize them to produce efficiency. An effective and efficient hierarchical chain partitioning(HCP) approach is used to find the optimal scene segmentation. HCP gets competitive performance at low tolerance and better performance at high tolerance The HCP method approaches 100 % performance at tolerance = 1. The output segments can be used for further analysis and processing of the videos that is for indexing, storage, searching and retrieval of particular portion of video. It can be used in several applications in the fields of Image processing, video content analysis, Computer vision, Movies, Medical, Sports, and News. We have tested the proposed methods in Feature Films, News videos, TV Shows and high degree of accuracy has been obtained.

This paper is mainly based on the video monitoring of unmanned substation, with the detection and tracking object of moving target, combined with the inter-frame difference method and background difference method, in order to achieve effective detection of moving targets through improving the traditional algorithm; combined with cam- shift algorithm and mean-shift algorithm to reduce the calculated amount and improve the accuracy of target tracking, in order to achieve intelligence level of substation video monitoring. This paper uses the computer vision and video analysis method to further analyze and understand the image sequences recorded by the camera, and links with related equipment to do simple processing while timely giving an alarm and feedback to monitoring personnel. In this way, the monitoring personnel can offer centralized processing for accident alarm, thus greatly reducing erroneous judgment and missing judgment of accidents, and improving the reliability, accuracy and timeliness of the alarm. Therefore, the intelligent video analysis is applied to substation attending, and combined with the traditional monitoring system, thus having a very important significance on security monitoring of the substation.

The intelligent transportation system under the big data environment is the development direction of the future transportation system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology and applies them to the entire ground transportation management system to establish a real‐time, accurate, and efficient comprehensive transportation management system that works on a large scale and in all directions. Intelligent video analysis is an important part of smart transportation. In order to improve the accuracy and time efficiency of video retrieval schemes and recognition schemes, this article firstly proposes a segmentation and key frame extraction method for video behavior recognition, using a multi‐time scale dual‐stream network to extract video features, improving the efficiency and efficiency of video behavior detection. On this basis, an improved algorithm for vehicle detection based on Faster R‐CNN is proposed, and the Faster R‐CNN network feature extraction layer is improved by using the principle of residual network, and a hole convolution is added to the network to filter out the redundant features of high‐resolution video images to improve the problem of vehicle missed detection in the original algorithm. The experimental results show that the key frame extraction technology combined with the optimized Faster R‐CNN algorithm model greatly improves the accuracy of detection and reduces the leakage. The detection rate is satisfactory.

Football video feature extraction and the coaching significance based on improved Huff coding model is analyzed in this manuscript. In the application of intelligent video analysis technology, it is easy to be affected by environmental illumination changes, target motion complexity, occlusion, and other factors, resulting in errors in the final target detection and tracking. However, with the advent of the big data era, the continuous innovation and development of science and technology have promoted development of intelligent video analysis technology and reduced the time and efficiency of the target information retrieval. The intelligent video analysis system is a combination of a modular design. The entire system is mainly composed of a video source, a core video intelligent processing engine, and a management terminal. The football video data set is integrated to construct the efficient processing system. The performance is then validated through the numerical simulation.

In this paper we propose a knowledge discovery and visualisation method for unearthing emotional states from physiological data typically available from wearable devices. In addition we investigate the viability of using a limited set of wearable sensors to extract decision tree rules which are representative of physiological changes taking place during emotional changes. Our method utilised a fusion of pre-processing and classification techniques using decision trees to discover logic rules relating to the valence and arousal emotional dimensions. This approach normalised the signal data in a manner that enabled accurate classification and generated logic rules for knowledge discovery. Furthermore, the use of three target classes for the emotional dimensions was effective at denoising the data and further enhancing classification and useful rule extraction. There are three key contributions in this work, firstly an exploration and validation of our knowledge discovery methodology, secondly successful extraction of high accuracy rules derived from physiological data and thirdly knowledge discovery and visualisation of relationships within-participant physiological data that can be inferred relating to emotions. Additionally, this work may be utilised in areas such as the medical sciences where interpretable rules are required for knowledge discovery.

Decision analysis and optimization are examples of inverse management problems which can be effectively handled by means of Artificial Intelligence (AT) and simulation techniques. However, the application of these techniques in specific industrial contexts finds major constraints in the lack of formalized methodologies and systematic procedures to retrieve and handle the data required for their implementation. The paper proposes Knowledge Discovery (KD) techniques as means of data pre-processing for the development of Al-based decision support systems (DSSs). This use of Knowledge Discovery techniques is illustrated in relation to the design of a hybrid DSS - combining AI and simulation techniques - for the management of maintenance services in the public transport sector.

In recent years both the number and the size of organisational databases have increased rapidly. However, although available processing power has also grown, the increase in stored data has not necessarily led to a corresponding increase in useful information and knowledge. This has led to a growing interest in the development of tools capable of harnessing the increased processing power available to better utilise the potential of stored data. The terms Knowledge Discovery in Databases and Mining have been adopted for a field of research dealing with the automatic discovery of knowledge implicit within databases. Data mining is useful in situations where the volume of data is either too large or too complicated for manual processing or, to a lesser extent, where human experts are unavailable to provide knowledge. The success already attained by a wide range of data mining applications has continued to prompt further investigation into alternative data mining techniques and the extension of data mining to new domains. This paper surveys, from the standpoint of the database systems community, current issues in data mining research by examining the architectural and process models adopted by knowledge discovery systems, the different types of discovered knowledge, the way knowledge discovery systems operate on different data types, various techniques for knowledge discovery and the ways in which discovered knowledge is used.

Most questions in the Fish4Knowledge project are related to the ability to count fish, fish species or events using video analysis Chap. 2. Automatic video analysis however brings uncertainty due to False Positive/False Negative classifications, which makes determining the counts based on automatic video analysis difficult. Automatic video analysis software is often able to express a measure of uncertainty for a single detection/recognition using a similarity score, indicating how certain the software is about a single decision. Logistic Regression allows us to combine these similarity scores to compute an estimated count, outperforming the estimates of the underlying machine learning methods used for the original video analysis. We show this works both for the two-class and multi-class problem. Furthermore, we identify potential pitfalls where ensuring a correct sampling procedure is essential. The error in the estimated species counts using Logistic Regression was on average around 15 on a set of 11,585 fish images, while the machine methods had an average error of 800. The key to understanding this huge improvement in accuracy is that we use Logistic Regression over the individual classifications to estimate the size of the whole population, rather than the species classification of any individual observation.

With the development of intelligent theory of computer vision and the popularization of surveillance cameras, intelligent video analysis and surveillance technology has been widely used. The traditional video monitoring system will produce a large amount of video information in the application process, which is not conducive to the storage and reference of information, and it can not carry out real-time warning, and has poor robustness to the bad environment. In view of these disadvantages, this paper proposes an improved video enhancement and concentration method, which has a good effect in ensuring the video concentration ratio and the target loss rate, and can improve the shortcomings brought by manual reference. Aiming at the problem of real-time early warning, an intelligent video analysis method is proposed to realize the functions of event alarm and detection analysis. It is of great significance to improve the video monitoring management level of the substation and realize the unattended operation of the substation.

In this paper, we give an overview of knowledge discovery (KD) and information extraction (IE) techniques on the World Wide Web (WWW). We intend to answer the following questions: What kind of additional uncertainty challenges are introduced by the WWW setting to basic KD and IE techniques? What are the fundamental techniques that can be used to reduce such uncertainty and achieve reasonable KD and IE performance on the WWW? What is the impact of each novel method? What types of interactions can be conducted between these techniques and information networks to make them benefit from each other? In what way can we utilize the results in more interesting applications? What are the remaining challenges and what are the possible ways to address these challenges? We hope this can provide a road map to advance KD and IE on the WWW to a higher level of performance, portability and utilization.

The increasing use of computers in all the areas of human activities is resulting in huge collections of digital data. Databases are common everywhere and are used as repositories of every kind of data. Knowledge discovery techniques and tools are used today to analyze those very large data sets to identify interesting patterns and trends in them. When data is maintained over geographically distributed sites the computational power of distributed and parallel systems can be exploited for knowledge discovery in databases. In this scenario the Grid can provide an effective computational support for distributed knowledge discovery on large data sets. To this purpose we designed a system called Knowledge Grid This chapter describes the Knowledge Grid architecture and discusses some related systems and models recently proposed for knowledge discovery on Grids. The chapter presents also how to design and implement distributed data mining applications by using the Knowledge Grid tools starting from searching Grid resources, composing software and data elements, and executing the resulting application on a Grid.

With the increasing popularity of Unmanned Aerial Vehicles (UAVs) in computer vision-related applications, intelligent UAV video analysis has recently attracted the attention of an increasing number of researchers. To facilitate research in the UAV field, this paper presents a UAV dataset with 100 videos featuring approximately 2700 vehicles recorded under unconstrained conditions and 840k manually annotated bounding boxes. These UAV videos were recorded in complex real-world scenarios and pose significant new challenges, such as complex scenes, high density, small objects, and large camera motion, to the existing object detection and tracking methods. These challenges have encouraged us to define a benchmark for three fundamental computer vision tasks, namely, object detection, single object tracking (SOT) and multiple object tracking (MOT), on our UAV dataset. Specifically, our UAV benchmark facilitates evaluation and detailed analysis of state-of-the-art detection and tracking methods on the proposed UAV dataset. Furthermore, we propose a novel approach based on the so-called Context-aware Multi-task Siamese Network (CMSN) model that explores new cues in UAV videos by judging the consistency degree between objects and contexts and that can be used for SOT and MOT. The experimental results demonstrate that our model could make tracking results more robust in both SOT and MOT, showing that the current tracking and detection methods have limitations in dealing with the proposed UAV benchmark and that further research is indeed needed.