SEIZE THE DATA. 2015
Transcript of SEIZE THE DATA. 2015
© Copyright 2015 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.1 SEIZE THE DATA. 2015
SEIZE THE DATA. 2015
SEIZE THE DATA. 2015
IDOL Rich Media (speech, image, and video) under the hoodDavid Humphrey / Month day, 2015
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
“The increasing use of multimedia… has contributed significantly to the growth of big data and will continue to do so.”
McKinsey Global Institute
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Where does IDOL Rich Media sit?
Ingest Enrich ProduceAnalyze
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Differing approaches
Phonetics search and indexing
Converting audio into raw sound
Pattern matching sounds for results
Word spotting search and indexing
Converting audio into words
Pattern matching words without meaning
Language model based Search & Indexing
Conceptual search
Converting audio into words and concepts
Statistical pattern matching the concepts with understanding
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Acoustic model, language model and lexicon for each language
IDOL Speech is supported by powerful algorithms
• 30+ languages supported
• Real-time operation
• Speaker Independent
• Ability to customize language
• Telephony and broadcast models
Models of fundamental sound patterns – different for low quality telephone models (8kHz) and higher quality broadcast models (16Khz+)
Base language models and customized models that include common phrases and word sequences
Trained pronunciationdictionary with
vocabulary
TextFront end processing
Recognition algorithm
Languagemodel
Lexicon
Acousticmodel
Speech
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Speech-to-text technology
Statistical models of speech and language
P(W) = probability of word string W
Language model
P(A|W) = probability of a acoustic sequence A given W
Acoustic model
Use Bayes rule to find the word string w that has the highest probability given the acoustic sequence
W = arg max P(W|A) = arg max P(W) P(A|W)P(A)
Language model Acoustic models
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Language model
Provides probability of word sequence
Forms a conceptual understanding of language
“Can I help you?”
“Can eye help you?”
Trained from large text corpora (100s of millions of words)
Defines words that can be recognized
Use training text, e.g. broadcast news
Encompasses topic information, colloquial phrases, etc.
Adaptable for particular customer
Specialist vocabulary, e.g. product names
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Acoustic model
Model the sounds that comprise a spoken language
Audio analyzed to extract energy at various frequencies
Dependent on audio format
16kHz sampled data for broadcast
8kHz sampled data for telephony
Complex statistical techniques model both the sounds and audio characteristics
State-of-the-art models for co-articulation: “could you” -> “cud ju”
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Supported languages
English• American
• British
• Australian
• Canadian
• Singaporean
French• European
• Canadian
German
Spanish• European
• North American
• South American
Italian
Danish
Dutch
Swedish
Flemish
Portuguese• Brazilian
• Portugal
Welsh
Catalan
Polish
Greek
Romanian
Czech
Slovak
Russian
Japanese
Korean
Mandarin
Arabic• Modern Standard
• Gulf
Farsi
Urdu
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Other functionality
Speaker ID
Spoken Language Identification
• Phonic sequences
Audio (phonetic) Phrase Search
• Phonic probalistic matches
Speech Segmentation
Transcript Alignment
Audio Classification
• Music, speech silence
Audio Quality
Audio Fingerprinting
• Key frequency points
Audio Security
• Glass, gunshot, scream
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Automatically monitor channels in many languages
Challenges with traditional solutions• Rapid increase in broadcast sources
• Exponential growth in number of online video sources
HP Broadcast Monitoring• Fully-automated, real-time, multilingual broadcast news monitoring across multiple sources
• Real-time transcription and translation of all available audio sources, 30+ available languages
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Basic terminology
Detection Categorization & classification
Recognition
Description Detecting that a particular type of object is present
Detecting objects of particular categories are in an image or classifying objects by feature
Identification of a particular instance of an object
Example • “There are 3 faces in this image”
• “This face looks middle aged.” • “The faces are Barack Obama, and George P. Burdell”
• “The logo shown here is the Hewlett Packard logo”
• “It looks like the Mona Lisa is in this photo”
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Find it. Analyse it.
Video and image analytics
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Find it. Analyse it.
Text OCR
ANPR
Video and image analytics
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Find it. Analyse it.
Text OCRIDR
ANPR
Barcodes
Video and image analytics
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Find it. Analyse it.
Text OCRIDR
ANPR
Faces
Person ID
People countDemographics
Barcodes
Video and image analytics
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Find it. Analyse it.
Text OCRIDR
ANPR
Faces
Person ID
People countClothing colours
Demographics
Barcodes
Colours
Video and image analytics
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Find it. Analyse it.
Text OCRIDR
ANPR
Faces
Person ID
People countClothing colours
Demographics
BarcodesObject match
Object classObjects
Colours
ISAS
Video and image analytics
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Find it
Building blocks of computer vision
Pre-process
Colour analysis
Grayscale conversion
Corner detection
Edge detection
Prior knowledge & rules
Texture analysis
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Find it
Tell-tale signs
y
|Δpx|
High contrast
|Δpx|
x
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Find it
Tell-tale signs
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OCR analytics
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Color
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Barcode and Qcode
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Face analytics
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Object analytics
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Image classification
The general problem:
Searching images against a large database for similarity of visual appearance
The specific issue:
What is the exact definition of similar?
Two approaches
Features then SVM
• Faster and easier to train
Neural Nets
• Better performance
• Much harder to train
Image Similarity Ranking
Less similar
Search source
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What is similar?
Trade mark infringement
Pornography detection for site blocking
Weapon identification for interactive training
Video scene indexing
Searching
Add insertion
Surveillance object type
Car/van/bus
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Scene classification
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Crowdsourced video and image classification
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Find it. Track it. Monitor it.
Video as a series of stills
Images Find text, faces, paintings, … (anything defined)
Repetition of Image analytics:• Combine results for greater accuracy.
• You found it, now track it.
• Something is there. Still there. Suggest it.
Video Find anything (that moves or moved)• Detect suspicious objects/events
• Improve human efficiency
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Video Management System (VMS) Video source• H264, H263, MPEG4, MJPEG, MPEG2
• Analogue, IP, Hybrid
• OnVif, RTSP, RTP/RTCP, UDP
• Unicast, Multicast and File
• MISB
Recording modes
Real-time, time lapse, event driven, VMD and “Lip-sync” Audio
Security
Digital signatures, LDAP mapped
Architecture
On premise, Cloud
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License plate recognition
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Used in association with ANPR
Vehicle recognition
Match Make and/or Model
• Easy to train
• Real-time matching
Alert or Search for Vehicle without registration
Validate database using ANPR result to identify illegal plated vehicles
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ANPR issues
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Features• One-to-one verification
• One to many identification
• Retrospective via automatic enrolment
Face recognition and demographics
Demographics
Found “President Obama” face
Body analysis Primary clothing color = whiteNot nude
Primary clothing color = whiteNot nude
Primary clothing color = blackNot nude
Face detection
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Use case: Police surveillance
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Use case: Counting and access control
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Demographic analysis
How many pixels do we need?
Method:• Collect high-res face images
• Rescale each to normalise eyeseparation to 150, 100, etc. pixels
Results:• Best performance is 80%
• Performance drops of a cliff with lessthan 15px between the eyes
Conclusion:NPG need four times resolution to reach the performance plateau
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
020406080100120140160
Acc
ura
cy r
ate
Eye separation/pixels
Combined
NPG data
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Scene analysisUnderstand what is happening in video
Detection of suspicious objects/events• Video motion detect
• Non-motion detect
• Tracking
• Behavioral
• Sequences
• Object identification
Key advantages• Improve human efficiency
• Not limited by human concentration span
• Eliminate staffing issues with 24 x 7 monitoring
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Restricted areas
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Restricted areas
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Railways
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Traffic
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False alarms or missed alarms
Precision = Recall =
False negatives True negatives
False positivesTrue positives
[ Relevant elements ]
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Classification of objects
Van - White Person(s)
Car - Silver
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Use case: Railway crossing
Business need
• Detect illegal rail crossing activity andautomatically generate evidence pack for court
• Drive down incidents by harsh enforcement
HP solution
• Validate warning light sequence and frequency
• Identify vehicles jumping the lights
• Capture license plate
• Transmit secure evidence pack
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Use case: Auckland Transport, New Zealand
Business need
• Improve public safety
• Pedestrians and cyclists
• Over 2000 cameras citywide
• Network of road and environmental sensors
• Real-time social media and news
HP solution
• Initial Phase: Detect high risk activities and investigate threats with scene analysis and licenseplate recognition
• Future Phase: Uncover breaking trends and facilitate incident responses with social media andbroadcast monitoring
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Use case: Race analysis
Business need
• Analyze broadcast video feeds in real-time
• Gain intelligence on other teams
• Gain understanding of audience
HP solution
• Identify in car cameras
• Identify teams
• Identify drivers
• Identify who is saying what
• Understand sentiment
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Scene analysis: Issues to be aware of
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Use case: Force protection
Business need
• Detect threats anytime, anywhere by correlatingintelligence from multiple sources in variousforms – audio, video, reports and 3rd partysensors
HP Autonomy ssolution
• Automatically flag anomalies by analyzing feedsfrom aerostat, UAV, towers, and correlating withother events
• Use biometric databases to relay real-timerecognition of facial features and license plates
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Small objects and occlusions
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Deviation and scaling
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UAV
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3D for tracking and modelling
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Autonomous flight
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Oil theft Niger delta: Coffer dams
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Media Management and Analytics Platform
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ProductsHistoric
• Video logger (engine)
• Image server (engine)
• Speech/Audio server (engine)
• Surveillance
• Wittwin (engine)
• ANPR (engine/app)
• ISAS (engine/app)
• Face (engine/app)
• Visor (app)
• ASA (app)
• Broadcast monitoring (app)
Current
• Video server
• Image server
• Speech/Audio server
• Video management and analysis platform
• Broadcast monitoring
Future
• Media server
• Media management and analysis platform
• Haven Analytics Application Framework
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HP MMAP architecture
Widgets
Restful API
Analytics Server ………….. Legacy
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Application architecture
Application
Analysis screens
HP MMAP
Video screen
Raw dataCircular bufferOn file system
Search widgets
Video
BD Platform
Video server
Big Data Platform
Video playerwidget
TranscriptWidget
Time Line widget
IDOL
Vertica
Dashboard widgets
JBOSS
CFS
Authorisation
OAuth
Identity provider
LDAP
Expose
d A
PI
REST
AP
I
Player sync’s other widgets
URL redirection
Source ID and UTC timeEvent ID
Video TS files (unsecure)
ThumbnailsMetadata
Indexs
Video TS filesMetadata
ACI
HLS playlist request(Can be distributed)
ACIRequest for Metadata
XML
Source ID
Extracted metadata
UTC time
Event ID
RESTFul (internal by widgets)
Request Metadata
Request Thumbnails
RESTFul
Request Source playlist URL
Enriched data
Token
User ID
Permissions
Custom
3rd Party ?
Video
Metadata
Video, Images, Metadata
Control
Data
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Video Server
New ACI server for video processing
Extracts metadata from video streams and outputs it to a variety of destinations
Multi-platform:
Linux 64 bits
Windows 64 bits
Unify multiple products• Focused product development
• Reuse common libraries
• Simplifies deployment
Video Server will be Media server
API Layer
VideoAudioImage
Actions
Metadata
Rolling buffer
File / StreamTransform
ESP Output
Analysis Encoder
Engines:
Ingest
Speech/Audio server
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Video Server Ingest Engines
Video Server ingests video through an ingest engine
Receives video input and produces demuxed and decoded streams producing image (Image_n) and audio (Audio_lang_n) tracks that can be processed by other engines
Currently we only have a single Ingest Engine:libAV
Virtually can ingest any video formatshttps://libav.org/general.html#Supported-File-Formats-and-Codecs.
[Ingest]
IngestEngine=LibAV
[LibAV]
Type=libav
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Analysis Engines
List of Analysis Engines included in VideoServer 10.8:
Barcode: Detect and read QR codes FaceDetect: Detect faces Demographics: Obtain demographic information such as age, gender, and ethnicity for detected faces FaceRecognize: Run face recognition on detected faces FaceState: Obtain additional information, such as facial expression, about detected faces Keyframe: Identify keyframesNumberPlate: Detect and read license plates on vehicles Object: Recognize known objects in video ObjectClass: Recognize known object classes in video OCR: Run Optical Character Recognition (OCR) SceneAnalysis: Run Intelligent Scene Analysis to identify important events SpeakerID: Identify speakers SpeechToText: Transcribe speech into text
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Event Stream Processing Engines
Attempts to identify meaningful events in streams of event data. Examples:
• Detect the occurrence of a particular word in an audio stream.
• Detect when two events occur within a specific time interval; for example, a number plate is detected within sixty seconds of a traffic light changing to red.
Video Server supports multiple ESP engine types:
• Logical:
− And, And Not, AndNotThen, AndThen, Or
− Deduplication
− Filter
• Lua scripting
ESP engines accept any track type. They also accept the output of other ESP engines.
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Encoding Engines
Video Server can encode the video it ingests and encode the video to files
List of Encoding Engines included in VideoServer 10.8:
• Imageencoder: Save image records to disk as image files.
• MPEG: Encode ingested video in MPEG format into disk
• Rollingbuffer Encode video to the rolling buffer into disk
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Output Engines
• Output engines transform metadata tracks produced by other engines into different formats and send the data to external systems such as IDOL Server, Vertica, …
• List of Output Engines included in VideoServer:
– Directory: Output information to a directory
– Httpserver: Store the latest record and return it in response to a HTTP request
– IDOL: Output information to IDOL Server
– Vertica: Output information to a Vertica database
– XML* Output information to XML File
– RestFul * Output information to web server
• XSL Transformation
• Index by Time or Event
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Output Engines
Output engines transform metadata tracks produced by other engines into different formats and send the data to external systems such as IDOL Server, Vertica, …
List of Output Engines included in VideoServer:
Directory: Output information to a directory
Httpserver: Store the latest record and return it in response to a HTTP request
IDOL: Output information to IDOL Server
Vertica: Output information to a Vertica database
XML* Output information to XML File
RestFul * Output information to web server
XSL Transformation
Index by time or event
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V2 Player V4 Player V6 Player Native HTML 5 Player
Plug-in technology Active-X + GeckoPepper API (chrome
only)Pepper API (chrome
only)-
Install required Yes No No No
Works in future browsers No Yes Yes Yes
UTC timestamps Yes Yes Yes No
Frame accurate Yes Yes Yes I Frame only
Live streams Yes Yes Yes Safari Only
HLS playback No Yes Yes Safari Only
Reverse playback Yes Yes Yes No
RTSP playback Yes No Yes No
UDP playback No No Yes No
Supported Codecs All standard types All standard types All standard types Browser dependent
File playback No Yes Yes Yes
Video Server Compatible No Yes Yes No
Wittwin Compatible Yes No Limited No
Uses browser cache No Yes Yes Yes
Uses browser proxy settings No Yes Yes Yes
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HP Video Player
Default Player HTML Template
AngularJS Directives
MediaElement Java Script API
HPBrowser
plugin V ?
Browser Native MediaElement
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TimeLine widget for web pages
Navigate by cursor
Mark inMark out
Display thumbnails
Grab a screen shot
Scroll & zoom
Seek by dateand time
Display metadata
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Garbage in garbage out
Image quality
Focus
Motion blur
Compression
Image field of view
Pixels on target POT matter
Relative motion
- Bad for ID
- Good for Events
Illumination
Day and night
Weather
Glare
- Winter sun
- Car headlights
IR cameras
Not if colour needed
Megapixel cameras
Great for POT
Bad for Bandwidth and CPU
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Bandwidth issues
Camera to server
Multicast
- Needs network support
Dual stream
- If camera supports
Reduced frame rate
- Possibly OK if planned
High compression
- Affects analytics
Server to Command and Control Centre (CC3)
Low res proxy
Evidential copy
C&C to users
Intranet
Web access
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OtherAlmost every opportunity in video/image analytics is unique in some way, so get sample video!
Sample images/videos, with specific statements of what is to be identified, are essential to qualify an opportunity.
If they have analogue cameras (old) they will not be very good and will make us look bad.
If this is not the customers first attempt, ask the right questions: who and why did they fail? It may just be impossible!
Identify issues early and flag to customer, do not try to overcome.
Use VMware all the time BUT for ease of deployment not resource sharing.
Proprietary compression with certain manufactures is a no go
We support all main manufactures of cameras but if we don’t have one then get us a loan unit as it doesn’t take long.
ANPR for new region may need new format and fonts, we have a process you follow.
Prior knowledge is our friend!
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Storage
Grandma to suck eggs!
1024 OS v’s 1000 Disk
Possibly 2 copies
Low res proxy
Evidential
Most recordings are now Full frame rate 24/7
Events just tag
Event driven recordings need pre and post
Storage period
UK legally 30days max, Internationally this varies massively
Evidence can be stored much longer
Business continuity
Raid default
Mirrored and offsite has been known but rare
General
Video standard PAL
Storage type JBOD
Choose Bit rate (MiBits/S) 2.00
Event recording
Channels per event 0
Days of events 0
Events per day 0
Event length Sec. 0
Frames per second 0.0
Event GBytes 0.0
Continuous recording
Channels of continuous 64
Days of continuous 30
Active hours per day 24
Frames per second 25.0
Continuous GBytes 47835.2
Total Storage TBytes 47.8
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Backup
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Main use case: Logos
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Logos: Easy
Advertising monitoring
Future Adds
Broadcast Monitoring
China Daily
Libray indexing
MediaBin
Police
Tatoo ID
ANPR
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Logos: Difficult
Too small
Too slow
Solution:
Tracking across video frames to over gaps(as Aurasma)
Algorithm optimisations
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Logos we don’t find
Perspective and Skew
Severe optical ‘bloom’
Complex background
Compression artefacts
Small size
Also:
Train for each view
Solid objects that don’t change shape (Animals)