14,419 research outputs found
Active Mining of Parallel Video Streams
The practicality of a video surveillance system is adversely limited by the
amount of queries that can be placed on human resources and their vigilance in
response. To transcend this limitation, a major effort under way is to include
software that (fully or at least semi) automatically mines video footage,
reducing the burden imposed to the system. Herein, we propose a semi-supervised
incremental learning framework for evolving visual streams in order to develop
a robust and flexible track classification system. Our proposed method learns
from consecutive batches by updating an ensemble in each time. It tries to
strike a balance between performance of the system and amount of data which
needs to be labelled. As no restriction is considered, the system can address
many practical problems in an evolving multi-camera scenario, such as concept
drift, class evolution and various length of video streams which have not been
addressed before. Experiments were performed on synthetic as well as real-world
visual data in non-stationary environments, showing high accuracy with fairly
little human collaboration
Recent Trends and Research Issues in Video Association Mining
With the ever-growing digital libraries and video databases, it is
increasingly important to understand and mine the knowledge from video database
automatically. Discovering association rules between items in a large video
database plays a considerable role in the video data mining research areas.
Based on the research and development in the past years, application of
association rule mining is growing in different domains such as surveillance,
meetings, broadcast news, sports, archives, movies, medical data, as well as
personal and online media collections. The purpose of this paper is to provide
general framework of mining the association rules from video database. This
article is also represents the research issues in video association mining
followed by the recent trends.Comment: 13 pages; 1 Figure; 1 Tabl
Breaking the Limits in Urban Video Monitoring: Massive Crowd Sourced Surveillance over Vehicles
Contemporary urban environments are in prompt need of means for intelligent
decision-making, where a crucial role belongs to smart video surveillance
systems. While existing deployments of stationary monitoring cameras already
deliver notable societal benefits, the proposed concept of massive video
surveillance over connected vehicles that we contribute in this paper may
further augment these important capabilities. We therefore introduce the
envisioned system concept, discuss its implementation, outline the high-level
architecture, and identify major data flows, while also offering insights into
the corresponding design and deployment aspects. Our conducted case study
confirms the potential of the described crowd sourced vehicular system to
effectively complement and eventually surpass even the best of today's static
video surveillance setups. We expect that our proposal will become of value and
integrate seamlessly into the future Internet-of-Things landscape, thus
enabling a plethora of advanced urban applications.Comment: 8 pages, 5 figures, accepted to IEEE Wireless Communications, 201
Compress-Store on Blockchain: A Decentralized Data Processing and Immutable Storage for Multimedia Streaming
Decentralization for data storage is a challenging problem for
blockchain-based solutions as the blocksize plays the key role for scalability.
In addition, specific requirements of multimedia data calls for various changes
in the blockchain technology internals.Considering one of the most popular
applications of secure multimedia streaming, i.e., video surveillance, it is
not clear how to judiciously encode incentivisation, immutability and
compression into a viable ecosystem. In this study, we provide a genuine scheme
that achieves this encoding for a video surveillance application. The proposed
scheme provides a novel integration of data compression, immutable off-chain
data storage using a new consensus protocol namely, proof of work storage
(PoWS) in order to enable fully useful work to be performed by the miner nodes
of the network. The proposed idea is the first step towards achieving greener
application of blockchain-based environment to the video storage business that
utilizes system resources efficiently.Comment: 7 pages, 3 figures, 1 table, submitted to IEEE Transactions on
services computin
Decentralized Smart Surveillance through Microservices Platform
Connected societies require reliable measures to assure the safety, privacy,
and security of members. Public safety technology has made fundamental
improvements since the first generation of surveillance cameras were
introduced, which aims to reduce the role of observer agents so that no
abnormality goes unnoticed. While the edge computing paradigm promises
solutions to address the shortcomings of cloud computing, e.g., the extra
communication delay and network security issues, it also introduces new
challenges. One of the main concerns is the limited computing power at the edge
to meet the on-site dynamic data processing. In this paper, a Lightweight IoT
(Internet of Things) based Smart Public Safety (LISPS) framework is proposed on
top of microservices architecture. As a computing hierarchy at the edge, the
LISPS system possesses high flexibility in the design process, loose coupling
to add new services or update existing functions without interrupting the
normal operations, and efficient power balancing. A real-world public safety
monitoring scenario is selected to verify the effectiveness of LISPS, which
detects, tracks human objects and identify suspicious activities. The
experimental results demonstrate the feasibility of the approach.Comment: 2019 SPIE Defense + Commercial Sensin
EIQIS: Toward an Event-Oriented Indexable and Queryable Intelligent Surveillance System
Edge computing provides the ability to link distributor users for multimedia
content, while retaining the power of significant data storage and access at a
centralized computer. Two requirements of significance include: what
information show be processed at the edge and how the content should be stored.
Answers to these questions require a combination of query-based search, access,
and response as well as indexed-based processing, storage, and distribution. A
measure of intelligence is not what is known, but is recalled, hence, future
edge intelligence must provide recalled information for dynamic response. In
this paper, a novel event-oriented indexable and queryable intelligent
surveillance (EIQIS) system is introduced leveraging the on-site edge devices
to collect the information sensed in format of frames and extracts useful
features to enhance situation awareness. The design principles are discussed
and a preliminary proof-of-concept prototype is built that validated the
feasibility of the proposed idea
Harnessing constrained resources in service industry via video analytics
Service industries contribute significantly to many developed and developing
- economies. As their business activities expand rapidly, many service
companies struggle to maintain customer's satisfaction due to sluggish service
response caused by resource shortages. Anticipating resource shortages and
proffering solutions before they happen is an effective way of reducing the
adverse effect on operations. However, this proactive approach is very
expensive in terms of capacity and labor costs. Many companies fall into
productivity conundrum as they fail to find sufficient strong arguments to
justify the cost of a new technology yet cannot afford not to invest in new
technologies to match up with competitors. The question is whether there is an
innovative solution to maximally utilize available resources and drastically
reduce the effect that the shortages of resources may cause yet achieving high
level of service quality at a low cost. This work demonstrates with a practical
analysis of a trolley tracking system we designed and deployed at Hong Kong
International Airport (HKIA) on how video analytics helps achieve management's
goal of satisfying customer's needs via real-time detection and prevention of
problems they may encounter during the service consumption process using
existing video technology rather than adopting new technologies. This paper
presents the integration of commercial video surveillance system with deep
learning algorithms for video analytics. We show that our system can provide
accurate decision when faced with total or partial occlusion with high accuracy
and it significantly improves daily operation. It is envisioned that this work
will heighten the appreciation of integrative technologies for resource
management within the service industries and as a measure for real-time
customer assistance.Comment: Accepted to appear in Archives of Industrial Engineering Journa
BlendMAS: A BLockchain-ENabled Decentralized Microservices Architecture for Smart Public Safety
Thanks to rapid technological advances in the Internet of Things (IoT), a
smart public safety (SPS) system has become feasible by integrating
heterogeneous computing devices to collaboratively provide public protection
services. While a service oriented architecture (SOA) has been adopted by IoT
and cyber-physical systems (CPS), it is difficult for a monolithic architecture
to provide scalable and extensible services for a distributed IoT based SPS
system. Furthermore, traditional security solutions rely on a centralized
authority, which can be a performance bottleneck or single point failure.
Inspired by microservices architecture and blockchain technology, this paper
proposes a BLockchain-ENabled Decentralized Microservices Architecture for
Smart public safety (BlendMAS). Within a permissioned blockchain network, a
microservices based security mechanism is introduced to secure data access
control in an SPS system. The functionality of security services are decoupled
into separate containerized microservices that are built using a smart
contract, and deployed on edge and fog computing nodes. An extensive
experimental study verified that the proposed BlendMAS is able to offer a
decentralized, scalable and secured data sharing and access control to
distributed IoT based SPS system.Comment: Submitted to the 2019 IEEE International Conference on Blockchain
(Blockchain-2019
AlertMix: A Big Data platform for multi-source streaming data
The demand for stream processing is increasing at an unprecedented rate. Big
data is no longer limited to processing of big volumes of data. In most
real-world scenarios, the need for processing stream data as it comes can only
meet the business needs. It is required for trading, fraud detection, system
monitoring, product maintenance and of course social media data such as Twitter
and YouTube videos. In such cases, a "too late architecture" that focuses on
batch processing cannot realize the use cases. In this article, we present an
end to end Big data platform called AlertMix for processing multi-source
streaming data. Its architecture and how various Big data technologies are
utilized are explained in this work. We present the performance of our platform
on real live streaming data which is currently handled by the platform.Comment: 9 pages, 4 figure
MacroBase: Prioritizing Attention in Fast Data
As data volumes continue to rise, manual inspection is becoming increasingly
untenable. In response, we present MacroBase, a data analytics engine that
prioritizes end-user attention in high-volume fast data streams. MacroBase
enables efficient, accurate, and modular analyses that highlight and aggregate
important and unusual behavior, acting as a search engine for fast data.
MacroBase is able to deliver order-of-magnitude speedups over alternatives by
optimizing the combination of explanation and classification tasks and by
leveraging a new reservoir sampler and heavy-hitters sketch specialized for
fast data streams. As a result, MacroBase delivers accurate results at speeds
of up to 2M events per second per query on a single core. The system has
delivered meaningful results in production, including at a telematics company
monitoring hundreds of thousands of vehicles.Comment: SIGMOD 201
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