mining frequent events from video
TRANSCRIPT
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MINING FREQUENT EVENTS FROM VIDEO
Steffi Keran Rani JM.E. Multimedia Technology
Anna University
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EVENT DETECTION Event detection involves the automatic organization of a
multimedia collection C into groups of items, each (group) of which corresponds to a distinct event.
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CHALLENGES1. requires application of several
Computer Vision
2. Involves subtleties that are readily
understood by humans, difficult to
encode for machine learning
approaches
3. Can be complicated due to clutter
in the environment, lighting, camera
placement, traffic, etc.
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APPLICATIONS
1. Video Surveillance
2. Video- on- Demand
3. Broadcast Video
4. Web Search
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CLUSTER CLASSIFICATION#
user
s /
#ph
otos
duration
[1 day, 2 users / 10 photos]
[2 years, 50 users / 120 photos]
#5
LANDMARK
EVENT
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EVENT DETECTION USING DATA MINING TECHNIQUES
Video
Video Parsing and Feature Detection
Instance Self Learning
Filtering and Reconstruction
Self Refining Training Dataset
Final DetectionDecision Tree Model
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VIDEO PARSING
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3 BUILDING BLOCKS1. Video Parsing and Feature Extraction
Involves temporal partitioning of the video sequence into meaningful units.
This module computes a large array of multimodal features (both visual and audio) from input videos
Five visual features are extracted for each shot:
1. pixel_change 2. histo_change;
3. background_mean 4. background_varr 5. dominant_color_ratio
2. Base ClassifiersMultiple base classifiers independently compute detection scores based on available features
3. Score FusionThis module combines multiple base classifier scores through diverse fusion methods, and
computes a single final detection score per video clip
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TWO- STEP PROCEDURE
1. Video content processing: The video clip is segmented into certain analysis units and their representative features are extracted.
2. Decision making: process that extracts the semantic index from the feature descriptors.
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DECISION MAKING PROCESSDECISION MAKING
Knowledge Based Approaches
Rule based Classifier
Statistical Approaches
Support Vector Machines
Dynamic Bayesian Network
C4.5 decision trees
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11
1. Event Detection Using Multi Modal Feature Fusion
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2. VIDEO EVENT DETECTION BY INFERRING TEMPORAL INSTANCE LABELS
Video recognition algorithm is inspired by proportion SVM (p-SVM), which explicitly models the latent unknown instance labels together with the known label proportions in a large-margin framework
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