SlideShare a Scribd company logo
A piXlogic White Paper

Sponsored by Flex Analytics




                                                                4984 El Camino Real
                                                                           Suite 205
                                                                Los Altos, CA 94022
                                                                    T. 650-967-4067
                                                                 info@piXlogic.com
                                                                  www.piXlogic.com




                                          Intelligent Image and Video Search
                                                     for Defense Applications




       Government Reseller for piXlogic
       10314 Thornbush Lane
       Bethesda, MD 20814
       (301) 787-2989




  Jul-2012
July 2012   pg. 2
Contents         Introduction
                                Images and videos have always been key
Introduction                3   elements of intelligence and defense
Problem Statement           3   operations. In recent years, the scope and
                                diversity of digital imagery has greatly
Previous Options            4
                                increased in every area: ground, satellite,
The piXserve Solution       5   UAV, surveillance, broadcast, etc. The
Key Features of piXserve    5   volume of material being acquired and
Security Applications       8   stored is staggering, with no visible plateau
                                in sight. Traditional methods of organizing,
Implementation              9
                                cataloguing, and distributing this material to
Summary                     9   analysts and the war-fighter are becoming
About piXlogic             10   impractical due to the scale involved. On
                                the other hand, timely access to nuggets of
                                vital information contained in images/videos
                                is key to operational success. The ability to
                                cross-correlate the information, whether it’s
                                being obtained from live sources or from
                                archived repositories, is more important than
                                ever.

                                In this environment, image/video search and
                                retrieval has become the new “must have”
                                element of any comprehensive solution.
                                Unfortunately, today’s image/video
                                management systems are not well suited to
                                help make sense of the data collected, and
                                can only provide, at best, very limited search
                                and retrieval capabilities.


                                Problem Statement
                                Most video management systems offer
                                limited options for automating processes
                                such as searching archived footage, or
                                generating alerts from live video. For the
                                most part, these features are either not
                                available, or only available in a very limited
                                sense. Often, a significant amount of
                                manpower is required to carry out even
                                simple search tasks. This is well known in
                                the field. Correlating visual data from




July 2012                                                                pg. 3
different sources is another very        generally in the same location on the image.
challenging task, mostly done            Both of these requirements limit the scope
manually today. Automated change         of applications possible with such systems.
detection is yet another largely
elusive goal.                            Face Recognition: Much as in the ALPR
                                         case, a big hurdle is to know where the face
Industry/government efforts during       to be measured is on the image. To solve
the last few years have focused on       this, typical systems require that the distance
building infrastructure and have         between the camera and the subject be
resulted in great improvements in the    within a predefined range. Lighting
ability to acquire higher resolution     variations are also critical which is why the
imagery/full motion video, moving        more successful implementations are limited
this material around the network         to indoor, entry-way, type of set-ups.
efficiently, and storing it. These are   Outdoor video in unconstrained
great accomplishments, but by            environments presents a challenge that is
themselves they are not enough.          outside the realm of most commercial
Now is the time to leverage previous     solutions available today.
investments and provide a much
needed level of automation so that       Object Detection: The ability to
analysts can deal with the size and      detect/recognize/search for specific objects
scale of the problems they face.         in a video or an image is not usually
However, for most solution               available. Some attempts have been made
providers, this remains a significant    for video, but the methods used are overly
technical challenge.                     simplistic and unreliable. A typical
                                         technique relies on “frame differencing” to
Previous Options                         separate moving things from a stationary
When automated video analysis tools      background. The idea is simple but
are available, they tend to be single-   unfortunately it only works in trivial
purpose with limited scope of            situations. If the camera is moving, the
applicability and stringent operating    background will move as well and frame
requirements. Consider the               differencing techniques won’t work.
following three examples:                Turning off a light, a cloud passing in the
                                         sky, a moving shadow, these are all things
Automated License Plate                  that can yield undesired results. Even when
Recognition: For most systems, the       the background and the camera are
hurdle is to know where the license      stationary, the amount of information that is
plate is in the image being analyzed.    obtained is limited. If the camera is
To circumvent this problem, solution     calibrated, some guess about the size of the
providers either require the use of      object can be made and from this an
specialized cameras (infrared) or that   inference can be derived about what is in the
the cameras be placed such that the      scene (perhaps an adult, may be not a dog),
license plate to be recognized is        but even this too can be quite unreliable (is


July 2012                                                                          pg. 4
it a dog, a tumbleweed, or a far away
person). Crowded environments
present a critical challenge to today’s   2. piXserve reasons about what it "saw"
systems.                                     in the image and develops an initial
                                             level of "understanding" about
                                             content and context. Where it can, it
The piXserve Solution                        automatically creates searchable
piXserve is a general-purpose                "tags" for what it saw in the image
image/video search and alerting              (piXlogic calls these tags "Notions").
solution. Breakthrough technology            For example, it can detect the
developed by piXlogic allows the             presence of things such as: sky,
software to automatically “see” the          vegetation, flower, face, building,
contents of an image/video frame             car, map, airplane, helicopter, etc.
and create a searchable index and
uses this information so that users       3. piXserve uses all the information
can search and create alerts in a very       calculated from the image to make
natural and logical way.                     comparisons between a search image
                                             and previously indexed
   1. piXserve automatically                 images/videos so that users can find
      “segments” an image in a               results that most closely match what
      way that discerns the                  they are looking for.
      individual objects in the
      image. It creates a                 4. piXserve can "see" not only visual
      mathematical description of            objects but also text strings that may
      the appearance of these                appear anywhere in the field of view
      objects "on the fly", and              of the image. This text is also
      stores it as a searchable index        indexed and made searchable.
      in a database.                         piXserve works with text from many
                                             languages (alphanumeric/latin-
                                             character based languages, Japanese,
                                             Korean, Chinese, etc.)

                                              5. Depending on the quality of the
                                             imagery involved and the type of
                                             search being done, piXserve has
                                             been designed to achieve accuracies
                                             in excess of 85%.

                                           Key Features of piXserve

                                               Automatic Indexing
                                             Point piXserve to a repository of


July 2012                                                                     pg. 5
images/video files or to a live       the mouse to point to an area of
       video feed, and automatically         the query image to indicate
       index content. No manual              which specific item(s) should
       intervention or data entry            be searched for.
       required.
                                          3. Browse the contents of existing
      Powerful Search                       databases, grab a frame “on the
       Through a web browser                 fly” from a video that is
       interface, users login to             playing, and use that frame to
       piXserve, connect to                  formulate a visual search
       available databases and               query.
       formulate search queries to
       retrieve desired images/video      4. Search images and videos by
       segments:                             object class ("Notion")

            1. Use an arbitrary image     5. Type a text string to search
               to search for                 pictures/videos where that
               images/video segments         string appears in the field of
               that contain the same or      view (a license plate, a street
               similar items                 sign, a name tag, etc.)

                                          6. Search for faces of specific
                                             individuals

                                          7. Perform not only simple but
                                             also complex multi-modal
                                             searches. (Example: find video
                                             sequences where something
                                             like the bag in this picture
                                             AND this face from this other
                                             picture AND this text string I
                                             just typed all appear in the field
                                                 of view at the same time.)
                                                 Use AND, OR, and NOT
                                                 operators to combine up to
                                                 6 criteria in a single query.


            2. U
               s
               e




July 2012                                                                 pg. 6
8. Search by file name                   and naming)

            9. Search by keyword or          Powerful Alerts
               other external metadata,       Create alert criteria just as you would
               if available.                  formulate a search query. piXserve-
                                              ALERT keeps track of what
            10. Submit sample images          piXserve machines on the network
                of non-deformable             are indexing and when a match is
                objects of interest and       made consistent with what the user
                automatically tag             specified, it generates a signal. The
                images/video frames           user receives an e-mail with a link to
                when these items are          the alert results. A JMS (Java
                visible.                      Messaging Service) signal is also
                                              generated to pass the alert on to other
      Powerful Automated Tagging             systems and applications for further
                                              action.
             1. Automatically tag
                images/video frames          Powerful Metadata
                with the name of              The richness of metadata calculated
                recognized                    by piXserve about each image/video
                individuals that              frame processed (objects and tags),
                appear therein                can be exploited to enable
                (automated face               customized applications that are of
                naming).                      high value in a variety of settings
                                              such as:
             2. Suggest keywords to
                describe the contents            1. Automatic determination of
                of a picture/video                  change detection when
                frame (automated                    videos taken at different
                keyword                             times from different angles
                recommendations)                    are compared.
                                                 2. Determining which portions
             3. Submit sample                       of a video archive contain
                images of non-                      useful information, and
                deformable objects of               which could be safely deleted
                interest and                        to minimize storage
                automatically tag                   requirements.
                images/video frames
                when these items are         Scaleable Architecture
                visible. (automated           piXserve is a multi-threaded, J2EE
                2D-object detection           scalable application that is suitable
                                              for the most demanding


July 2012                                                                       pg. 7
implementations.                   piXserve extends the capabilities of today’s
                                          systems by adding the ability to
      Web Services API                   automatically analyze the video that is being
       A REST-based API package           collected and stored. These video streams
       is available to support            can be intercepted by piXserve and analyzed
       integrations with third party      for alerting purposes. Similarly, recorded
       applications and workflow          video can be analyzed, searched and
       environments.                      correlated using piXserve. The analytical
                                          capabilities in piXserve support: facial
Security Applications                     recognition, general purpose object
If you are concerned with the cost,       detection and recognition, text recognition,
speed, and accuracy of your video         license plate recognition, automatic tagging,
investigative work, whether it be         and more. All the indexing work is done
forensic in nature or dealing with        automatically, server side, in the
live situations, then you should          background. Users are then free to create
consider piXserve as a “must-have”        visually-based search criteria and navigate
add-on to your current system.            the body of accumulated material. They can
                                          do all of this “on the fly”, as they see fit at
Conventional systems focus on             the moment, based on whatever problem or
managing and manipulating cameras         situation they are dealing with.
and storage devices. Unfortunately,
they only provide limited capabilities    The piXserve search environment is
for searching the captured video:         intuitive and productive, and the user
time, date, motion, transaction           interface is through a web browser (Internet
trigger…these are among the more          Explorer, Mozilla Firefox, Safari, Google
common set of options available.          Chrome, or equivalent). Users can drag-
While useful, these features alone are    and-drop a picture from anywhere to
inadequate to support a productive        formulate a similarity search query, or pause
workflow and significant manpower         a video while it’s playing, and use that
effort is required even for the simpler   frame to create a new search criteria or
tasks. Common situations involve          refine an existing one. This latter capability
several operators having to stare at a    greatly simplifies the discovery process
bank of monitors for hours on end in      precisely in those situations when the user
order to catch an event of interest, or   isn’t quite sure what they are looking for and
having to wade through hundreds of        are working in an investigative/exploratory
hours of video from many cameras          mode.
looking for a specific event or trying
to correlate separate ones. These
situations are labor intensive, error
prone, and do not scale well.




July 2012                                                                           pg. 8
Implementation
piXserve can process videos in a
variety of formats (MPEG-1, MPEG-
2, MPEG-4, H-263, H-264, etc.).
piXserve can also process still
images in over 90 different formats
(jpeg, tiff, png, bmp, psd, etc.)
piXserve can index both archived
video as well as live video broadcast
from Multicast IP cameras. piXserve
and piXserve-ALERT run on
standard 2-CPU rack servers (multi-
core Intel-Xeon processors or
equivalent), in a Windows Server
2003 or 2008 environment.
Customers typically choose Dell or
HP hardware for implementation.         parallelize throughput and serve growing
piXserve is available in both x32 and   needs.
x64 bit versions.
In order to index archived video        The metadata created by piXserve is stored
piXserve requires that the storage      in an RDBMs (Oracle or MS-SQL are
device be accessible via a network      supported, PostgreSQL is bundled with
share (Linux/Unix/Windows).             piXserve). The data and the piXserve output
Further, the stored video should not    can be integrated/correlated to that from
be in a proprietary, non-standard       other systems that the customer may be
format.                                 using. The alerting functionality is provided
                                        by piXserve-ALERT. A single instance of
A single server can process large       piXserve-ALERT can serve many users and
amounts of archived material, or live   monitor potentially thousands of alert
video from multiple feeds/sources.      criteria. Here too scaling is achieved by
The higher the number of cores on       adding additional ALERT servers. In
the server, the higher the number of    configurations were several hundreds or
hours of video per day that can be      thousands of individuals will be searching
processed by a single machine.          piXserve generated data, the use of
piXserve implementations can range      piXserve-Enterprise Edition is
in size, from as little as a single     recommended.
server to scalable multi-server and
distributed configurations. The         Summary
architecture of the product is such     Images and videos are a critical element of
that as the needs of the customer       defense and intelligence operations. It is
grow, hardware can be added to          very difficult to deal with an ever-growing
                                        amount of captured video without


July 2012                                                                       pg. 9
automation. The alternatives to                   Content Auto-tagging (automatically
automation are expensive, time                     label an image/video)
consuming, and prone to errors. At                Content Alerting (automatically inform
the same time, there is a lack of                  users when items of interest appear in a
suitable tools to provide a
                                                   live video stream or web crawl)
meaningful level of real-world
automation.                                       Content Change Detection
                                                   (automatically compare images and
piXserve provides an unparalleled                  video segments to detect changes at
level of image analysis and                        the object level)
understanding. In a single tool it
provides capabilities that span:            piXlogic serves the needs of government
object detection and recognition,           and industrial customers. piXlogic sells its
face recognition, license plate             products directly and through a network of
recognition, text recognition,              resellers in the US, the UK, Japan, Australia,
automatic tagging, and more. In             Argentina, Israel, and Italy.
each of these areas, piXserve
redefines the state of the art and can      Corporate
help your meet the efficiency and           piXlogic, Inc.         T. +1-650-967-4067
                                            4984 El Camino Real    E. info@piXlogic.com
effectiveness goals that you have set       Suite 205              W. www.piXlogic.com
for yourself.                               Los Altos, CA 94022

                                            Flex Analytics is a systems integrator and
About piXlogic                              software reseller in the U.S. Intelligence
piXlogic is a privately held company        Community. It supports the sale,
located in Los Altos, CA, USA, the          implementation and customization of
heart of Silicon Valley. piXlogic is        piXserve in government installations.
an In-Q-Tel portfolio company (a
venture capital organization that           Government Sales       +1-301-787-2989
                                            Flex Analytics LLC     gpepus@flexanalytics.com
serves the needs of the US
                                            10314 Thornbush Ln     www.flexanalytics.com
Intelligence Community). The                Bethesda, MD 20814
company’s flagship products are
piXserve and piXserve-ALERT.
The software enables:

      Content Discovery (find
       pictures/videos that contain
       specific objects, scenes, text, or
       people of interest)




July 2012                                                                           pg. 10

More Related Content

Similar to Defense applications white paper

IRJET- Real-Time Object Detection System using Caffe Model
IRJET- Real-Time Object Detection System using Caffe ModelIRJET- Real-Time Object Detection System using Caffe Model
IRJET- Real-Time Object Detection System using Caffe Model
IRJET Journal
 
IRJET- Object Detection in an Image using Deep Learning
IRJET- Object Detection in an Image using Deep LearningIRJET- Object Detection in an Image using Deep Learning
IRJET- Object Detection in an Image using Deep Learning
IRJET Journal
 
How deep learning could revolutionize broadcastin
How deep learning could revolutionize broadcastinHow deep learning could revolutionize broadcastin
How deep learning could revolutionize broadcastin
ShadekulIslamShovo
 
A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...
A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...
A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...
Dejan Kovachev
 
Secure IoT Systems Monitor Framework using Probabilistic Image Encryption
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionSecure IoT Systems Monitor Framework using Probabilistic Image Encryption
Secure IoT Systems Monitor Framework using Probabilistic Image Encryption
IJAEMSJORNAL
 
Unified VSS white paper
Unified VSS white paperUnified VSS white paper
Unified VSS white paper
Monique DiCarlo
 
小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵
小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵
小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵
CHENHuiMei
 
IRJET- Object Detection in Real Time using AI and Deep Learning
IRJET- Object Detection in Real Time using AI and Deep LearningIRJET- Object Detection in Real Time using AI and Deep Learning
IRJET- Object Detection in Real Time using AI and Deep Learning
IRJET Journal
 
50120130404055
5012013040405550120130404055
50120130404055
IAEME Publication
 
Query clip genre recognition using tree pruning technique for video retrieval
Query clip genre recognition using tree pruning technique for video retrievalQuery clip genre recognition using tree pruning technique for video retrieval
Query clip genre recognition using tree pruning technique for video retrieval
IAEME Publication
 
Query clip genre recognition using tree pruning technique for video retrieval
Query clip genre recognition using tree pruning technique for video retrievalQuery clip genre recognition using tree pruning technique for video retrieval
Query clip genre recognition using tree pruning technique for video retrieval
IAEME Publication
 
IRJET- Object Detection and Recognition using Single Shot Multi-Box Detector
IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET- Object Detection and Recognition using Single Shot Multi-Box Detector
IRJET- Object Detection and Recognition using Single Shot Multi-Box Detector
IRJET Journal
 
What to curate? Preserving and Curating Software-Based Art
What to curate? Preserving and Curating Software-Based ArtWhat to curate? Preserving and Curating Software-Based Art
What to curate? Preserving and Curating Software-Based Art
neilgrindley
 
Content Modelling for Human Action Detection via Multidimensional Approach
Content Modelling for Human Action Detection via Multidimensional ApproachContent Modelling for Human Action Detection via Multidimensional Approach
Content Modelling for Human Action Detection via Multidimensional Approach
CSCJournals
 
IRJET- Object Detection and Recognition for Blind Assistance
IRJET- Object Detection and Recognition for Blind AssistanceIRJET- Object Detection and Recognition for Blind Assistance
IRJET- Object Detection and Recognition for Blind Assistance
IRJET Journal
 
Automatic Visual Concept Detection in Videos: Review
Automatic Visual Concept Detection in Videos: ReviewAutomatic Visual Concept Detection in Videos: Review
Automatic Visual Concept Detection in Videos: Review
IRJET Journal
 
YOLOv4: A Face Mask Detection System
YOLOv4: A Face Mask Detection SystemYOLOv4: A Face Mask Detection System
YOLOv4: A Face Mask Detection System
IRJET Journal
 
Democratizing AI with Apache Spark
Democratizing AI with Apache SparkDemocratizing AI with Apache Spark
Democratizing AI with Apache Spark
Spark Summit
 
Voice Enable Blind Assistance System -Real time Object Detection
Voice Enable Blind Assistance System -Real time Object DetectionVoice Enable Blind Assistance System -Real time Object Detection
Voice Enable Blind Assistance System -Real time Object Detection
IRJET Journal
 
Mobile Visual Search
Mobile Visual SearchMobile Visual Search

Similar to Defense applications white paper (20)

IRJET- Real-Time Object Detection System using Caffe Model
IRJET- Real-Time Object Detection System using Caffe ModelIRJET- Real-Time Object Detection System using Caffe Model
IRJET- Real-Time Object Detection System using Caffe Model
 
IRJET- Object Detection in an Image using Deep Learning
IRJET- Object Detection in an Image using Deep LearningIRJET- Object Detection in an Image using Deep Learning
IRJET- Object Detection in an Image using Deep Learning
 
How deep learning could revolutionize broadcastin
How deep learning could revolutionize broadcastinHow deep learning could revolutionize broadcastin
How deep learning could revolutionize broadcastin
 
A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...
A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...
A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...
 
Secure IoT Systems Monitor Framework using Probabilistic Image Encryption
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionSecure IoT Systems Monitor Framework using Probabilistic Image Encryption
Secure IoT Systems Monitor Framework using Probabilistic Image Encryption
 
Unified VSS white paper
Unified VSS white paperUnified VSS white paper
Unified VSS white paper
 
小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵
小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵
小數據如何實現電腦視覺,微軟AI研究首席剖析關鍵
 
IRJET- Object Detection in Real Time using AI and Deep Learning
IRJET- Object Detection in Real Time using AI and Deep LearningIRJET- Object Detection in Real Time using AI and Deep Learning
IRJET- Object Detection in Real Time using AI and Deep Learning
 
50120130404055
5012013040405550120130404055
50120130404055
 
Query clip genre recognition using tree pruning technique for video retrieval
Query clip genre recognition using tree pruning technique for video retrievalQuery clip genre recognition using tree pruning technique for video retrieval
Query clip genre recognition using tree pruning technique for video retrieval
 
Query clip genre recognition using tree pruning technique for video retrieval
Query clip genre recognition using tree pruning technique for video retrievalQuery clip genre recognition using tree pruning technique for video retrieval
Query clip genre recognition using tree pruning technique for video retrieval
 
IRJET- Object Detection and Recognition using Single Shot Multi-Box Detector
IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET- Object Detection and Recognition using Single Shot Multi-Box Detector
IRJET- Object Detection and Recognition using Single Shot Multi-Box Detector
 
What to curate? Preserving and Curating Software-Based Art
What to curate? Preserving and Curating Software-Based ArtWhat to curate? Preserving and Curating Software-Based Art
What to curate? Preserving and Curating Software-Based Art
 
Content Modelling for Human Action Detection via Multidimensional Approach
Content Modelling for Human Action Detection via Multidimensional ApproachContent Modelling for Human Action Detection via Multidimensional Approach
Content Modelling for Human Action Detection via Multidimensional Approach
 
IRJET- Object Detection and Recognition for Blind Assistance
IRJET- Object Detection and Recognition for Blind AssistanceIRJET- Object Detection and Recognition for Blind Assistance
IRJET- Object Detection and Recognition for Blind Assistance
 
Automatic Visual Concept Detection in Videos: Review
Automatic Visual Concept Detection in Videos: ReviewAutomatic Visual Concept Detection in Videos: Review
Automatic Visual Concept Detection in Videos: Review
 
YOLOv4: A Face Mask Detection System
YOLOv4: A Face Mask Detection SystemYOLOv4: A Face Mask Detection System
YOLOv4: A Face Mask Detection System
 
Democratizing AI with Apache Spark
Democratizing AI with Apache SparkDemocratizing AI with Apache Spark
Democratizing AI with Apache Spark
 
Voice Enable Blind Assistance System -Real time Object Detection
Voice Enable Blind Assistance System -Real time Object DetectionVoice Enable Blind Assistance System -Real time Object Detection
Voice Enable Blind Assistance System -Real time Object Detection
 
Mobile Visual Search
Mobile Visual SearchMobile Visual Search
Mobile Visual Search
 

Defense applications white paper

  • 1. A piXlogic White Paper Sponsored by Flex Analytics 4984 El Camino Real Suite 205 Los Altos, CA 94022 T. 650-967-4067 info@piXlogic.com www.piXlogic.com Intelligent Image and Video Search for Defense Applications Government Reseller for piXlogic 10314 Thornbush Lane Bethesda, MD 20814 (301) 787-2989 Jul-2012
  • 2. July 2012 pg. 2
  • 3. Contents Introduction Images and videos have always been key Introduction 3 elements of intelligence and defense Problem Statement 3 operations. In recent years, the scope and diversity of digital imagery has greatly Previous Options 4 increased in every area: ground, satellite, The piXserve Solution 5 UAV, surveillance, broadcast, etc. The Key Features of piXserve 5 volume of material being acquired and Security Applications 8 stored is staggering, with no visible plateau in sight. Traditional methods of organizing, Implementation 9 cataloguing, and distributing this material to Summary 9 analysts and the war-fighter are becoming About piXlogic 10 impractical due to the scale involved. On the other hand, timely access to nuggets of vital information contained in images/videos is key to operational success. The ability to cross-correlate the information, whether it’s being obtained from live sources or from archived repositories, is more important than ever. In this environment, image/video search and retrieval has become the new “must have” element of any comprehensive solution. Unfortunately, today’s image/video management systems are not well suited to help make sense of the data collected, and can only provide, at best, very limited search and retrieval capabilities. Problem Statement Most video management systems offer limited options for automating processes such as searching archived footage, or generating alerts from live video. For the most part, these features are either not available, or only available in a very limited sense. Often, a significant amount of manpower is required to carry out even simple search tasks. This is well known in the field. Correlating visual data from July 2012 pg. 3
  • 4. different sources is another very generally in the same location on the image. challenging task, mostly done Both of these requirements limit the scope manually today. Automated change of applications possible with such systems. detection is yet another largely elusive goal. Face Recognition: Much as in the ALPR case, a big hurdle is to know where the face Industry/government efforts during to be measured is on the image. To solve the last few years have focused on this, typical systems require that the distance building infrastructure and have between the camera and the subject be resulted in great improvements in the within a predefined range. Lighting ability to acquire higher resolution variations are also critical which is why the imagery/full motion video, moving more successful implementations are limited this material around the network to indoor, entry-way, type of set-ups. efficiently, and storing it. These are Outdoor video in unconstrained great accomplishments, but by environments presents a challenge that is themselves they are not enough. outside the realm of most commercial Now is the time to leverage previous solutions available today. investments and provide a much needed level of automation so that Object Detection: The ability to analysts can deal with the size and detect/recognize/search for specific objects scale of the problems they face. in a video or an image is not usually However, for most solution available. Some attempts have been made providers, this remains a significant for video, but the methods used are overly technical challenge. simplistic and unreliable. A typical technique relies on “frame differencing” to Previous Options separate moving things from a stationary When automated video analysis tools background. The idea is simple but are available, they tend to be single- unfortunately it only works in trivial purpose with limited scope of situations. If the camera is moving, the applicability and stringent operating background will move as well and frame requirements. Consider the differencing techniques won’t work. following three examples: Turning off a light, a cloud passing in the sky, a moving shadow, these are all things Automated License Plate that can yield undesired results. Even when Recognition: For most systems, the the background and the camera are hurdle is to know where the license stationary, the amount of information that is plate is in the image being analyzed. obtained is limited. If the camera is To circumvent this problem, solution calibrated, some guess about the size of the providers either require the use of object can be made and from this an specialized cameras (infrared) or that inference can be derived about what is in the the cameras be placed such that the scene (perhaps an adult, may be not a dog), license plate to be recognized is but even this too can be quite unreliable (is July 2012 pg. 4
  • 5. it a dog, a tumbleweed, or a far away person). Crowded environments present a critical challenge to today’s 2. piXserve reasons about what it "saw" systems. in the image and develops an initial level of "understanding" about content and context. Where it can, it The piXserve Solution automatically creates searchable piXserve is a general-purpose "tags" for what it saw in the image image/video search and alerting (piXlogic calls these tags "Notions"). solution. Breakthrough technology For example, it can detect the developed by piXlogic allows the presence of things such as: sky, software to automatically “see” the vegetation, flower, face, building, contents of an image/video frame car, map, airplane, helicopter, etc. and create a searchable index and uses this information so that users 3. piXserve uses all the information can search and create alerts in a very calculated from the image to make natural and logical way. comparisons between a search image and previously indexed 1. piXserve automatically images/videos so that users can find “segments” an image in a results that most closely match what way that discerns the they are looking for. individual objects in the image. It creates a 4. piXserve can "see" not only visual mathematical description of objects but also text strings that may the appearance of these appear anywhere in the field of view objects "on the fly", and of the image. This text is also stores it as a searchable index indexed and made searchable. in a database. piXserve works with text from many languages (alphanumeric/latin- character based languages, Japanese, Korean, Chinese, etc.) 5. Depending on the quality of the imagery involved and the type of search being done, piXserve has been designed to achieve accuracies in excess of 85%. Key Features of piXserve  Automatic Indexing Point piXserve to a repository of July 2012 pg. 5
  • 6. images/video files or to a live the mouse to point to an area of video feed, and automatically the query image to indicate index content. No manual which specific item(s) should intervention or data entry be searched for. required. 3. Browse the contents of existing  Powerful Search databases, grab a frame “on the Through a web browser fly” from a video that is interface, users login to playing, and use that frame to piXserve, connect to formulate a visual search available databases and query. formulate search queries to retrieve desired images/video 4. Search images and videos by segments: object class ("Notion") 1. Use an arbitrary image 5. Type a text string to search to search for pictures/videos where that images/video segments string appears in the field of that contain the same or view (a license plate, a street similar items sign, a name tag, etc.) 6. Search for faces of specific individuals 7. Perform not only simple but also complex multi-modal searches. (Example: find video sequences where something like the bag in this picture AND this face from this other picture AND this text string I just typed all appear in the field of view at the same time.) Use AND, OR, and NOT operators to combine up to 6 criteria in a single query. 2. U s e July 2012 pg. 6
  • 7. 8. Search by file name and naming) 9. Search by keyword or  Powerful Alerts other external metadata, Create alert criteria just as you would if available. formulate a search query. piXserve- ALERT keeps track of what 10. Submit sample images piXserve machines on the network of non-deformable are indexing and when a match is objects of interest and made consistent with what the user automatically tag specified, it generates a signal. The images/video frames user receives an e-mail with a link to when these items are the alert results. A JMS (Java visible. Messaging Service) signal is also generated to pass the alert on to other  Powerful Automated Tagging systems and applications for further action. 1. Automatically tag images/video frames  Powerful Metadata with the name of The richness of metadata calculated recognized by piXserve about each image/video individuals that frame processed (objects and tags), appear therein can be exploited to enable (automated face customized applications that are of naming). high value in a variety of settings such as: 2. Suggest keywords to describe the contents 1. Automatic determination of of a picture/video change detection when frame (automated videos taken at different keyword times from different angles recommendations) are compared. 2. Determining which portions 3. Submit sample of a video archive contain images of non- useful information, and deformable objects of which could be safely deleted interest and to minimize storage automatically tag requirements. images/video frames when these items are  Scaleable Architecture visible. (automated piXserve is a multi-threaded, J2EE 2D-object detection scalable application that is suitable for the most demanding July 2012 pg. 7
  • 8. implementations. piXserve extends the capabilities of today’s systems by adding the ability to  Web Services API automatically analyze the video that is being A REST-based API package collected and stored. These video streams is available to support can be intercepted by piXserve and analyzed integrations with third party for alerting purposes. Similarly, recorded applications and workflow video can be analyzed, searched and environments. correlated using piXserve. The analytical capabilities in piXserve support: facial Security Applications recognition, general purpose object If you are concerned with the cost, detection and recognition, text recognition, speed, and accuracy of your video license plate recognition, automatic tagging, investigative work, whether it be and more. All the indexing work is done forensic in nature or dealing with automatically, server side, in the live situations, then you should background. Users are then free to create consider piXserve as a “must-have” visually-based search criteria and navigate add-on to your current system. the body of accumulated material. They can do all of this “on the fly”, as they see fit at Conventional systems focus on the moment, based on whatever problem or managing and manipulating cameras situation they are dealing with. and storage devices. Unfortunately, they only provide limited capabilities The piXserve search environment is for searching the captured video: intuitive and productive, and the user time, date, motion, transaction interface is through a web browser (Internet trigger…these are among the more Explorer, Mozilla Firefox, Safari, Google common set of options available. Chrome, or equivalent). Users can drag- While useful, these features alone are and-drop a picture from anywhere to inadequate to support a productive formulate a similarity search query, or pause workflow and significant manpower a video while it’s playing, and use that effort is required even for the simpler frame to create a new search criteria or tasks. Common situations involve refine an existing one. This latter capability several operators having to stare at a greatly simplifies the discovery process bank of monitors for hours on end in precisely in those situations when the user order to catch an event of interest, or isn’t quite sure what they are looking for and having to wade through hundreds of are working in an investigative/exploratory hours of video from many cameras mode. looking for a specific event or trying to correlate separate ones. These situations are labor intensive, error prone, and do not scale well. July 2012 pg. 8
  • 9. Implementation piXserve can process videos in a variety of formats (MPEG-1, MPEG- 2, MPEG-4, H-263, H-264, etc.). piXserve can also process still images in over 90 different formats (jpeg, tiff, png, bmp, psd, etc.) piXserve can index both archived video as well as live video broadcast from Multicast IP cameras. piXserve and piXserve-ALERT run on standard 2-CPU rack servers (multi- core Intel-Xeon processors or equivalent), in a Windows Server 2003 or 2008 environment. Customers typically choose Dell or HP hardware for implementation. parallelize throughput and serve growing piXserve is available in both x32 and needs. x64 bit versions. In order to index archived video The metadata created by piXserve is stored piXserve requires that the storage in an RDBMs (Oracle or MS-SQL are device be accessible via a network supported, PostgreSQL is bundled with share (Linux/Unix/Windows). piXserve). The data and the piXserve output Further, the stored video should not can be integrated/correlated to that from be in a proprietary, non-standard other systems that the customer may be format. using. The alerting functionality is provided by piXserve-ALERT. A single instance of A single server can process large piXserve-ALERT can serve many users and amounts of archived material, or live monitor potentially thousands of alert video from multiple feeds/sources. criteria. Here too scaling is achieved by The higher the number of cores on adding additional ALERT servers. In the server, the higher the number of configurations were several hundreds or hours of video per day that can be thousands of individuals will be searching processed by a single machine. piXserve generated data, the use of piXserve implementations can range piXserve-Enterprise Edition is in size, from as little as a single recommended. server to scalable multi-server and distributed configurations. The Summary architecture of the product is such Images and videos are a critical element of that as the needs of the customer defense and intelligence operations. It is grow, hardware can be added to very difficult to deal with an ever-growing amount of captured video without July 2012 pg. 9
  • 10. automation. The alternatives to  Content Auto-tagging (automatically automation are expensive, time label an image/video) consuming, and prone to errors. At  Content Alerting (automatically inform the same time, there is a lack of users when items of interest appear in a suitable tools to provide a live video stream or web crawl) meaningful level of real-world automation.  Content Change Detection (automatically compare images and piXserve provides an unparalleled video segments to detect changes at level of image analysis and the object level) understanding. In a single tool it provides capabilities that span: piXlogic serves the needs of government object detection and recognition, and industrial customers. piXlogic sells its face recognition, license plate products directly and through a network of recognition, text recognition, resellers in the US, the UK, Japan, Australia, automatic tagging, and more. In Argentina, Israel, and Italy. each of these areas, piXserve redefines the state of the art and can Corporate help your meet the efficiency and piXlogic, Inc. T. +1-650-967-4067 4984 El Camino Real E. info@piXlogic.com effectiveness goals that you have set Suite 205 W. www.piXlogic.com for yourself. Los Altos, CA 94022 Flex Analytics is a systems integrator and About piXlogic software reseller in the U.S. Intelligence piXlogic is a privately held company Community. It supports the sale, located in Los Altos, CA, USA, the implementation and customization of heart of Silicon Valley. piXlogic is piXserve in government installations. an In-Q-Tel portfolio company (a venture capital organization that Government Sales +1-301-787-2989 Flex Analytics LLC gpepus@flexanalytics.com serves the needs of the US 10314 Thornbush Ln www.flexanalytics.com Intelligence Community). The Bethesda, MD 20814 company’s flagship products are piXserve and piXserve-ALERT. The software enables:  Content Discovery (find pictures/videos that contain specific objects, scenes, text, or people of interest) July 2012 pg. 10