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Cost-effective Video Analytics in Smart Cities

2.
Abstract
Market Trends and Challenges
The Solution
a) Distributed Architecture / Leveraging the built-in
features of the Camera System
b) Configurable Analytics System
Case Study
Common Issues
Conclusion
Reference
Author Info
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TABLE OF CONTENTS

3.
Abstract
Market Trends and Challenges
As technology abounds, the Smart City trend is taking shape in many countries. Its implementation comes with some challenges,
though - traffic congestion, public safety, and the integration of various infrastructures through analyzed data. When we say “smart”,
the first thing that comes to mind is an intelligent system made possible through the analysis of huge chunks of data. Video would be
the most effective data form for analysis and for making quick or real time response systems in Smart Cities, whether it is related to
safety, security, crowd management, traffic management, smart retail processes or healthcare. Video analytics is a powerful tool that
has the potential to convert unstructured video data into structured useful data which can be analyzed, searched, and managed to
create a real-time intelligent response system. The introduction of intelligent video analytics in Smart City applications also comes with
some challenges, such as scalability, reliability, speed, and cost-effectiveness.
The global video analytics market has changed dramatically over the years. The trend
has moved from standalone to networked solutions, from safety and security to other
uses in verticals like retail, healthcare, traffic management, education, sports and in
numerous other fields. As more sophisticated situations arise out of the Smart City
concept, and as the need for intelligent security systems arise due to rising criminal/
terrorist activity, cost-effective, efficient & intelligent video analytics appear to be the
need of the hour.
In 2025, it is expected we will have around 26 global Smart Cities and around 50
percent of these will be located in North America and Europe [2]. Analysts forecast that
the video analytics market will grow at a CGR of 34.12 percent over the period of
2013-2018 and grow to $867.8 million by 2017. Latin America, and Middle East and
Africa regions are emerging markets, whereas Europe, Asia-Pacific, and North America
regions are considered high growth markets [1].

4.
An intelligent Video analytics is predominantly implemented in two different configurations – server-based analytics, and edge-based
analytics. With video analytics moving to the cloud and for online video analytics, the major challenge would be to reduce the load on
a carrier network. If we compress videos, their quality is compromised due to both the compression and the transmission of the data
on the network. In addition, uncompressing the video at the server adds an extra processor demand on the server, which decreases
its efficiency for analytics. Another challenge that arises in server-based analytics is the increase in processing demand on the server,
particularly as all video analytics processing is processor intensive and takes place on the server. Therefore, when adding more
cameras, either the server needs to be upgraded or system restructuring needs to be done for adding extra servers.
With Smart Cities taking shape, intelligent security and surveillance, smart traffic management and smart retail is becoming extremely
important. Advanced technology for crime management by installing video surveillance systems in Smart Cities will cost around 10
percent of the overall cost to set up the city [3]. Currently, network and camera infrastructure exists for video surveillance. Reusing the
existing resources would help reduce the investment and make video analytics solutions more cost-effective. So, edge-based
analytics, which would require replacing existing cameras with high-end smart cameras with analytical features, would not prove to be
beneficial in this regard. In addition, the limitation in processing power in these devices compromises the efficiency and performance
of video analytics.
Video analytics is useful across all verticals. In security and surveillance, it can be used for object detection, asset security, loitering
detection, overcrowding identification, emergency response scenarios; yet in all these cases, the challenge of false alarms creep in.
Moreover, algorithm accuracy in such analytics and in more sophisticated analytics such as face recognition, license plate recognition,
and vehicle direction and count for traffic management, has always been a concern for service providers providing video analytics
services. All these challenges - of reducing carrier network load, hardware dependency for video processing, limited parallel processing
on servers, reusing existing camera and network infrastructure, accuracy of algorithms in various environments, and false alarms, have
been the key inhibitors of the video analytics market.

5.
To meet the challenges of server-based analytics we can have distributed architecture for video analytics. In this system, the workload
of the server would be distributed among the edge devices and the server. For this approach, edge devices like cameras, smart
phones, smart tablets, etc. will have some analytics capability like motion, object, and color detection, distinguishing objects from
environmental noise, and detecting moving or stationary objects which, in turn, would create metadata that would be transferred over
the network, separately from the digital video stream, to the server for further analysis.
The Solution
a) Distributed Architecture / Leveraging the built-in features of
the Camera System
Edge Devices
with basic
Analytics
Any
Network
Advanced
Analytics
Configurable
Alerting
Missing Objects
Left behind Objects
New Objects
Object Movement
Object Count
Cost Effective Customizable
Algorithms Hardware
Independence Intituve Configuration
Comprehensive Realtime
& Offline Video Analytics
Any
Usecase
Smart Cities
Smart Retail
Transportation
Metadata

6.
b) Configurable Analytics System
The server would be configurable to set the conditions in the recorder for the alarm system. Server processing would include receiving
and storing metadata from edge devices and extracting the objects which match the conditions already set.It would also be responsible
for reporting the result and creating custom alert systems which have been pre-configured. Once the alert has been raised, the user
can be given a choice to stream or store only the events which are cause for concern. All this would help reduce the load on the carrier
network and help in customizing storage.
come of the existing cameras being used for video analytics already have basic analytic capabilities like motion detection, lighting
detection and more, while some cameras may not. Reusing such existing infrastructure later would require severs to do most of the
analytics. In such scenarios, we need to design processing on servers in such a way that adding more cameras should not pose more
challenges like server upgradation or restructuring. For this to be achieved, existing video analytics frameworks need to be
re-architectured in such a way that server processing is distributed to reduce the load on any particular server. Also, breaking down the
processing and analytics steps in a modular manner will help to scale them up independently, based on need. This would help in cost
reduction because the same hardware would be capable of handling more cameras and each component of the framework would run
on any commodity hardware without the requirement of high-end servers for small scale operations.
Video analytics algorithms depend a lot on the environment for analysis, on face
recognition factors like lighting, background, and face orientation due to changing
emotions, which often leads to inaccuracy. These inaccuracies and false alarms can
be dealt with if the user has the option to configure some parameters depending on
the situation for which the analytics has to be performed. And based on these
configurations, customized algorithms or best suited algorithms can be applied. This
can certainly help us enhance the accuracy of the algorithms and reduce the false
alarms.

7.
Almost 60 percent of the world’s population is expected to live in urban areas by 2025, increasing the growth of Smart Cities, which
would entail a need for innovative and efficient technologies to foster intelligent systems. Video data and analysis would play a major
role in designing such systems. Existing city cameras can act as sensors for activity and the input from these cameras can be fed into
customized algorithms for analysis.
The most important aspect of Smart Cities would be intelligent security and surveillance. With video analytics, remote and unmanned
monitoring is possible. For example, there would be no need for manual monitoring, or unattended object detection, or for securing
valuable assets, or for illegal parking, or for intruder detection. All this can be efficiently and automatically done by using configurable
video analytics which can help in the reduction of false alarms, and more importantly, in leveraging real time custom alerts so that
proper and timely action can be taken. This can also be applied in many other cases such as loitering detection, crowd monitoring,
people counting, vandalism, queue management, and more. Face recognition systems can help us reduce criminal activities by
helping us nab criminals faster.
Case Study
Intelligent traffic management would be another important
aspect of Smart Cities. Vehicle counting in high traffic areas can
help in rerouting to prevent traffic congestion. License plate
recognition helps in reducing traffic rule violations. Wrong way,
Illegal parking, speed zones, and vehicle tracking are some of
the other applications of video analytics, which will help in
creating a smart traffic system with real time traffic assistance

8.
A major challenge in the solution would be concerning the accuracy of the algorithms and the reduction of false alarms. As in unattended
object detection, the configuration parameters should be carefully chosen based on the scenario it has to be used in. For example,
configuration parameters would vary when cameras are placed in crowded areas, as chances of false alarms increase in such scenarios.
For face recognition, the function of matching faces from a database should be optimized for better analytical performance since the
database contains thousands of images.
As various modules of frequently used analytics frameworks are closely coupled, it is indeed challenging to re-architect these frameworks
to make them hardware independent and enable them to run on any commodity hardware.
To achieve more intelligent systems, large amounts of data need to be collected from across the city,
at each instant, and then analyzed to extract useful information to make decisions and create an
intelligent response system. Smart City solution providers will face challenges in increasing network
load due to the huge amounts of video data flowing through their networks. Then comes the
integration of video analytics with the city’s existing infrastructure and the algorithms, depending on
the scenarios in which they are used.
Video analytics has been in the market for a long time but must scale up with the changing trends and
sophisticated requirements. For cost-effective analytics, distributed architecture with user control
seems to be a good solution. The architecture must leverage the built in features of existing cameras,
thereby reducing the need for setting up infrastructure from scratch. This video analytics platform
identifies the most accurate algorithms/components depending on the configuration, and is able to
report the results and create custom alerts in a pre-configured way to attain better accuracy. For
efficiency and independence from hardware, video analytics processing can be distributed across
commodity hardware, which will help reduce cost by avoiding the need for high-end servers. In Smart
Cities with varying applications of video analytics solutions in fields such as security systems, utilities
operators, and emergency response systems, it gives users a simple way to pick the feed they would
like, instrument the analysis they want, and report the way they require in a simple-configurable
manner.
Common Issues
Conclusion

9.
This whitepaper is published by HCL Engineering and R&D Services.
The views and opinions in this article are for informational purposes only and should not be considered as a substitute for professional
business advice. The use herein of any trademarks is not an assertion of ownership of such trademarks by HCL nor intended to imply
any association between HCL and lawful owners of such trademarks.
For more information about HCL Engineering and R&D Services,
Please visit http://www.hcltech.com/engineering-rd-services
Copyright@ HCL Technologies
All rights reserved.
http://www.marketsandmarkets.com/Market-Reports/intelligent-video-analytics-market-778.html
http://www.forbes.com/sites/sarwantsingh/2014/06/19/smart-cities-a-1-5-trillion-market-opportunity/
http://computer.financialexpress.com/columns/futuristic-techology-to-secure-smart-cities/10483/
Reference
Author Info
Anurag Choubey
HCL Engineering and R&D Services
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10.
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