Video Database Management System Research Articles

SketchQL: Video Moment Querying with a Visual Query Interface

Localizing video moments based on the movement patterns of objects is an important task in video analytics. Existing video analytics systems offer two types of querying interfaces based on natural language and SQL, respectively. However, both types of interfaces have major limitations. SQL-based systems require high query specification time, whereas natural language-based systems require large training datasets to achieve satisfactory retrieval accuracy. To address these limitations, we present SketchQL, a video database management system (VDBMS) for offline, exploratory video moment retrieval that is both easy to use and generalizes well across multiple video moment datasets. To improve ease-of-use, SketchQL features a visual query interface that enables users to sketch complex visual queries through intuitive drag-and-drop actions. To improve generalizability, SketchQL operates on object-tracking primitives that are reliably extracted across various datasets using pre-trained models. We present a learned similarity search algorithm for retrieving video moments closely matching the user's visual query based on object trajectories. SketchQL trains the model on a diverse dataset generated with a novel simulator, that enhances its accuracy across a wide array of datasets and queries. We evaluate SketchQL on four real-world datasets with nine queries, demonstrating its superior usability and retrieval accuracy over state-of-the-art VDBMSs.

Optimizing Video Queries with Declarative Clues

Video Database Management Systems (VDBMS) leverage advancements in computer vision and deep learning for efficient video data analysis and retrieval. This paper introduces the concept of user-specified Clues, allowing users to incorporate domain-specific knowledge, referred to as Clues, into query optimization. Clues are expressed as Clue types, each associated with optimization rules, and applied to queries through Clue instances. The extensible ClueVQS system we present to incorporate these ideas, optimizes queries automatically, utilizing Clues to improve processing efficiency. We also introduce algorithms to optimize queries using Clues allowing for trade-offs between speed and query accuracy. Our proposals and system address challenges such as data-dependent Clue effectiveness, limiting search space, and accuracy-efficiency trade-offs. Detailed experimental results demonstrate query speedups of up to two orders of magnitude compared to other applicable approaches, and a reduction of the query optimizer time by up to 95% while respecting user-specified accuracy constraints, showcasing the effectiveness of the proposed framework.

Seiden: Revisiting Query Processing in Video Database Systems

State-of-the-art video database management systems (VDBMSs) often use lightweight proxy models to accelerate object retrieval and aggregate queries. The key assumption underlying these systems is that the proxy model is an order of magnitude faster than the heavyweight oracle model. However, recent advances in computer vision have invalidated this assumption. Inference time of recently proposed oracle models is on par with or even lower than the proxy models used in state-of-the-art (SoTA) VDBMSs. This paper presents Seiden, a VDBMS that leverages this radical shift in the runtime gap between the oracle and proxy models. Instead of relying on a proxy model, Seiden directly applies the oracle model over a subset of frames to build a query-agnostic index, and samples additional frames to answer the query using an exploration-exploitation scheme during query processing. By leveraging the temporal continuity of the video and the output of the oracle model on the sampled frames, Seiden delivers faster query processing and better query accuracy than SoTA VDBMSs. Our empirical evaluation shows that Seiden is on average 6.6 x faster than SoTA VDBMSs across diverse queries and datasets.

Octree-Based 3D Logic and Computation of Spatial Relationships in Live Video Query Processing

Live video computing (LVC) on distributed smart cameras has many important applications; and a database approach based on a Live Video DataBase Management System (LVDBMS) has shown to be effective for general LVC application development. The performance of such a database system relies on accurate interpretation of spatial relationships among objects in the live video. With the popularity of affordable depth cameras, 3D spatial computation techniques have been applied. However, the 3D object models currently used are expensive to compute, and offer limited scalability. We address this drawback in this article by proposing an octree-based 3D spatial logic and presenting algorithms for computing 3D spatial relationships using depth cameras. To support continuous query processing on live video streams, we also develop a GPU-based implementation of the proposed technique to further enhance scalability for real-time applications. Extensive performance studies based on a public RGB-D dataset as well as the LVDBMS prototype demonstrates the correctness and efficiency of our techniques.

HAH-tree: towards a multidimensional index structure supporting different video modelling approaches in a video database management system

This paper proposes a multidimensional distance-based index structure for video data which supports the three important video modelling approaches namely hierarchical unit-based modelling, feature-based modelling and video semantics modelling seamlessly within one single framework. These three modelling techniques collectively capture and contain the important aspects of the users' information need during content-based video retrieval. The index is built based on the low-level features of the video data, and the hierarchical containment relationships as well as the video semantics are introduced into the index space with an efficient data signature and a stochastic model, respectively. Efficient k-NN algorithms are proposed to emulate popular content-based video retrieval approaches in a multidimensional distance-based index structure. Extensive experimental results demonstrate the capability of the index structure to generate relevant query results with low computational overhead.

Real-Time Query Processing on Live Videos in Networks of Distributed Cameras

With the proliferation of inexpensive cameras and the availability of high-speed wired and wireless networks, systems of distributed cameras are becoming an enabling technology for a broad range of interdisciplinary applications in domains such as public safety and security, manufacturing, transportation, and healthcare. Today’s live video processing systems on networks of distributed cameras, however, are designed for specific classes of applications. To provide a generic query processing platform for applications of distributed camera networks, the authors designed and implemented a new class of general purpose database management systems, the live video database management system (LVDBMS). The authors view networked video cameras as a special class of interconnected storage devices, and allow the user to formulate ad hoc queries expressed over real-time live video feeds. This paper introduces their system and presents the live video data model, the query language, and the query processing and optimization technique.

Automatic detection of salient objects and spatial relations in videos for a video database system

Multimedia databases have gained popularity due to rapidly growing quantities of multimedia data and the need to perform efficient indexing, retrieval and analysis of this data. One downside of multimedia databases is the necessity to process the data for feature extraction and labeling prior to storage and querying. Huge amount of data makes it impossible to complete this task manually. We propose a tool for the automatic detection and tracking of salient objects, and derivation of spatio-temporal relations between them in video. Our system aims to reduce the work for manual selection and labeling of objects significantly by detecting and tracking the salient objects, and hence, requiring to enter the label for each object only once within each shot instead of specifying the labels for each object in every frame they appear. This is also required as a first step in a fully-automatic video database management system in which the labeling should also be done automatically. The proposed framework covers a scalable architecture for video processing and stages of shot boundary detection, salient object detection and tracking, and knowledge-base construction for effective spatio-temporal object querying.

A histogram-based approach for object-based query-by-shape-and-color in image and video databases

Considering the fact that querying by low-level object features is essential in image and video data, an efficient approach for querying and retrieval by shape and color is proposed. The approach employs three specialized histograms, (i.e. distance, angle, and color histograms) to store feature-based information that is extracted from objects. The objects can be extracted from images or video frames. The proposed histogram-based approach is used as a component in the query-by-feature subsystem of a video database management system. The color and shape information is handled together to enrich the querying capabilities for content-based retrieval. The evaluation of the retrieval effectiveness and the robustness of the proposed approach is presented via performance experiments.

BilVideo: Design and Implementation of a Video Database Management System

With the advances in information technology, the amount of multimedia data captured, produced, and stored is increasing rapidly. As a consequence, multimedia content is widely used for many applications in today’s world, and hence, a need for organizing this data, and accessing it from repositories with vast amount of information has been a driving stimulus both commercially and academically. In compliance with this inevitable trend, first image and especially later video database management systems have attracted a great deal of attention, since traditional database systems are designed to deal with alphanumeric information only, thereby not being suitable for multimedia data.In this paper, a prototype video database management system, which we call BilVideo, is introduced. The system architecture of BilVideo is original in that it provides full support for spatio-temporal queries that contain any combination of spatial, temporal, object-appearance, external-predicate, trajectory-projection, and similarity-based object-trajectory conditions by a rule-based system built on a knowledge-base, while utilizing an object-relational database to respond to semantic (keyword, event/activity, and category-based), color, shape, and texture queries. The parts of BilVideo (Fact-Extractor, Video-Annotator, its Web-based visual query interface, and its SQL-like textual query language) are presented, as well. Moreover, our query processing strategy is also briefly explained.

Rule-based spatiotemporal query processing for video databases

In our earlier work, we proposed an architecture for a Web-based video database management system (VDBMS) providing an integrated support for spatiotemporal and semantic queries. In this paper, we focus on the task of spatiotemporal query processing and also propose an SQL-like video query language that has the capability to handle a broad range of spatiotemporal queries. The language is rule-based in that it allows users to express spatial conditions in terms of Prolog-type predicates. Spatiotemporal query processing is carried out in three main stages: query recognition, query decomposition, and query execution.

BilVideo: a video database management system

The BilVideo video database management system provides integrated support for spatiotemporal and semantic queries for video. A knowledge base, consisting of a fact base and a comprehensive rule set implemented in Prolog, handles spatio-temporal queries. These queries contain any combination of conditions related to direction, topology, 3D relationships, object appearance, trajectory projection, and similarity-based object trajectories. The rules in the knowledge base significantly reduce the number of facts representing the spatio-temporal relations that the system needs to store. A feature database stored in an object-relational database management system handles semantic queries. To respond to user queries containing both spatio-temporal and semantic conditions, a query processor interacts with the knowledge base and object-relational database and integrates the results returned from these two system components. Because of space limitations, we only discuss the Web-based visual query interface and its fact-extractor and video-annotator tools. These tools populate the system's fact base and feature database to support both query types.

Accommodating hybrid retrieval in a comprehensive video database management system

A comprehensive video retrieval system should be able to accommodate and utilize various (complementary) description data in facilitating effective retrieval. We advocate a hybrid retrieval approach by integrating a query-based (database) mechanism with content-based retrieval (CBR) functions. We describe the VideoMAP/sup +/ architecture, discuss issues related to developing such a comprehensive video database management system, and its specific language mechanism (CAROL/ST with CBR) which provides an improved expressive power than pure query-based or CBR methods currently offer. We also describe an experimental prototype being developed based on a commercial object-oriented toolkit using VC++ and Java.

Semantic Extraction and Semantics-Based Annotation and Retrieval for Video Databases

Digital video databases have become more pervasive and finding video clips quickly in large databases becomes a major challenge. Due to the nature of video, accessing contents of video is difficult and time-consuming. With content-based video systems today, there exists a significant gap between the user's information and what the system can deliver. Therefore, enabling intelligent means of interpretation on visual content, semantics annotation and retrieval are important topics of research. In this paper, we consider semantic interpretation of the contents as annotation tags for video clips, giving a retrieval-driven and application-oriented semantics extraction, annotation and retrieval model for video database management system. This system design employs an algorithm on objects' relation and it can reveal the semantics defined with fast real-time computation.

Spatial and temporal content-based access to hypervideo databases

Providing content-based video query, retrieval and browsing is the most important goal of a video database management system (VDBMS). Video data is unique not only in terms of its spatial and temporal characteristics, but also in the semantic associations manifested by the entities present in the video. This paper introduces a novel video data model called Logical Hypervideo Data Model. In addition to multilevel video abstractions, the model is capable of representing video entities that users are interested in (defined as hot objects) and their semantic associations with other logical video abstractions, including hot objects themselves. The semantic associations are modeled as video hyperlinks and video data with such property are called hypervideo. Video hyperlinks provide a flexible and effective way of browsing video data. Based on the proposed model, video queries can be specified with both temporal and spatial constraints, as well as with semantic descriptions of the video data. The characteristics of hot objects' spatial and temporal relations and efficient evaluation of them are also discussed. Some query examples are given to demonstrate the expressiveness of the video data model and query language. Finally, we describe a modular video database system architecture that our web-based prototype is based on.

Scene change detection techniques for video database systems

Scene change detection (SCD) is one of several fundamental problems in the design of a video database management system (VDBMS). It is the first step towards the automatic segmentation, annotation, and indexing of video data. SCD is also used in other aspects of VDBMS, e.g., hierarchical representation and efficient browsing of the video data. In this paper, we provide a taxonomy that classifies existing SCD algorithms into three categories: full-video-image-based, compressed-video-based, and model-based algorithms. The capabilities and limitations of the SCD algorithms are discussed in detail. The paper also proposes a set of criteria for measuring and comparing the performance of various SCD algorithms. We conclude by discussing some important research directions.

A dynamic data model for a video database management system

A dynamic data model for a video database management system