a real-time big data architecture for glasses detection using computer vision techniques
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A Real-Time Big Data Architecture For Glasses Detection Using Computer Vision Techniques
Alberto Fernández, Rubén Casado, Rubén Usamentiaga
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OUTLINE
● Introduction● Algorithm for glasses detection● Big Data architecture for glasses detection● Conclusions● Future work
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INTRODUCTION● The rise of Internet, IoT and Cloud Computing has
led to an impressive growth of data● Increasing information gathered by low-cost
information sensing devices● Domain-specific information collected by
organizations should be analyzed automatically
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LOGS SENSORS CAMERAS MICROPHONESMOBILE
DEVICES
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INTRODUCTION● SQL-based DB perfect for storing and processing
structured data but not prepared for Big Data● Big Data is characterized by the 3Vs
○ Volume: the size of the data to be processed○ Velocity: frequency of the data generation, dynamic
aspects of the data and generating the results in RT.○ Variety: multimodal nature of data:
■ different data schemas of data source■ structured data (ontologies)■ unstructured data (sensors signals)
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INTRODUCTION● Big Data architectures can be classified into:
○ Batch processing (not real time)■ Efficient way of processing high volumes of data
collected during a period of time. ■ Information collected into “batches” as a unit
○ Stream processing■ Continuous input > processing > output data ■ low-response time achieved at the expense of less
rigorous analysis of data○ Hybrid processing
■ Batch and stream processing results are required■ Results are merged, synchronized and composed
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INTRODUCTION● Video/image generated by sensors and devices has
become the largest source○ Processing surveillance videos for information extraction
requires real-time stream processing○ The video data requires to get processed on time to extract
the full benefit of surveillance:■ warning in case of emergency■ something wrong is detected
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INTRODUCTION● Big Data architecture for streaming processing of
large amounts of images is proposed:○ A real-time scalable system for automatic glasses
detection using video images.● Contributions
○ A scalable low-latency architecture for image analysis using Big Data technologies
○ Parallelization of a glasses detection strategy○ Parametrized to detect other face attributes
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PROPOSED SYSTEM AND ARCHITECTURE
● Glasses detection on face images● Big data architecture for glasses detection on face
images
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GLASSES DETECTION ON FACE IMAGES
● Image acquisition● Face detection● Preprocessing of detected face ● Get the feature sets● Classify features
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Imageacquisition
Face detection
Pre- processing
Build features
Classifi- cation
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GLASSES DETECTION ON FACE IMAGES
● Image acquisition○ Read frame from input video○ Convert it to grayscale
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GLASSES DETECTION ON FACE IMAGES● Face detection
○ Viola & Jones algorithm is used:■ robust (high detection rate:high TP and very low FP) ■ executed in real time
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GLASSES DETECTION ON FACE IMAGES● Preprocessing of detected face in order to deal with:
○ pose, rotation, scale and inaccuracies of located face○ noisepiece is placed at the same level as the eyes both in
height and width
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GLASSES DETECTION ON FACE IMAGES● Get the feature sets: Local Binary Pattern (LBP)
○ LBP is a well-known technique in computer vision■ LBP is a type of feature used for classification
○ LBP histogram (LBPH) is usually built for texture classification○ LBPH into mxn regions is calculated to get spatial information
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LBP
LBP
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GLASSES DETECTION ON FACE IMAGES● Classify features
○ Support Vector Machine (SVM) is applied to classify the feature sets
○ SVMs are a useful technique for data classification■ have been proven useful in many pattern recognition
tasks i.e. face recognition
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GLASSESNO GLASSES
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LBP histogram
BIG DATA ARCHITECTURE● Big Data architecture is proposed● Parallelize the different steps of the glasses
detection workflows.○ Topology implemented with a streaming technology
Apache Storm○ Storm is a distributed real-time computation system
released as open source by Twitter● Parametrized to detect other face
attributes
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BIG DATA ARCHITECTUREArchitecture in Storm: two elements● Spouts read information from the source and emit
the data as K-V tuples○ Reads from a message broker (RabbitMQ, Kafka) or
streaming API● Bolts process information coming from the spouts
or other bolts.● Storm defines topologies connecting bolts and
spouts to process in streamingSPOUT represented as
BOLTrepresented as
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BIG DATA ARCHITECTURE
VideoSpout:● Split the video streaming into a sequence of images
(frames).○ This Spout uses a shuffle grouping
■ Frames are randomly distributed across the next bolts■ Each bolt is guaranteed to get an equal number of
frames
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BIG DATA ARCHITECTURE
V&Bolt:● Viola & Jones algorithm is applied for each frame● The output of this algorithm is estimated positions
of detected faces as rectangles
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BIG DATA ARCHITECTURE
NormalizationBolt:● From each rectangle, it calculates the region
around the eyes● Returns this region to next step
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BIG DATA ARCHITECTURE
LBPHBolt:● LBP operator is applied to the normalized region● A spatially enhanced histogram is constructed● These features are used in the next step
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BIG DATA ARCHITECTURE
SVMBolt:● Support Vector Machine (SVM) is applied on the
classification step.● Glasses/no glasses classification will be finally
obtained
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BIG DATA ARCHITECTURE● Closed/open eyes classifier using the same architecture
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BIG DATA ARCHITECTURE● Smile classifier using another normalization bolt
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BIG DATA ARCHITECTURE● Gender classifier using another normalization bolt
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BIG DATA ARCHITECTURE
● Type of glasses using another classification bolt
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SPORT GLASSES
SAFETY GLASSES
SUNGLASSES
GOOGLE GLASSES
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CONCLUSIONS
● Real-time Big Data architecture○ Collect, maintain and analyze massive volumes of
images○ It can be modified in order to detect other attributes:
■ smile, gender, age or face recognition classifiers
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FUTURE WORK
● Deep Learning○ Deep Learning algorithms in our pipeline detection
architecture will be proposed
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Thanks for your attention
Alberto Ferná[email protected]
A Real-Time Big Data Architecture For Glasses Detection Using Computer Vision Techniques
Alberto Fernández, Rubén Casado, Rubén Usamentiaga