Real-time and Retrospective Analysis of Video Streams and Still Image Collections using MPEG-7 Ganesh Gopalan, College of Oceanic and Atmospheric Sciences,

Real-time and Retrospective Analysis of Video Streams and

Still Image Collections using MPEG-7

Ganesh Gopalan,

College of Oceanic and Atmospheric Sciences, Oregon State University

Introduction

• HD video streams have potential to improve understanding of deep sea eco-systems

• However, volume and complexity associated with the HD streams and formats can be overwhelming

• Our approach: Use industry standards to transform video into a data type vs. treating it as viewing material

MPEG-7 Overview

• Multimedia content description interface

• Consists of low-level descriptors and high-level description schemes

• Low-level descriptors provide statistical information about the pixel values in content

• Description Schemes are used to represent semantic information

Low Level Descriptors

• Structures that describe content in terms of the distribution of edges, colors, textures, shapes and motion

• Descriptors extracted using MPEG-7 Experimental Model (XM) software

• The input is a still image or a frame from video

• The output is an XML description of the statistical information

Examples of Low Level Descriptors

• Edge Histogram• Homogeneous Texture• Color Layout• Color Structure• Motion Activity

• Descriptors are rotation and scaling invariant

Descriptor Extraction and Search

• Phase 1: descriptor XML for collection of frames/still images is generated and cached

• Phase 2: difference between query image descriptor from those values cached in phase one is computed

• The cache can be augmented with the descriptors from a new video or still image collection

Description Schemes

• Description Schemes attempt to model the reality behind the content

• Low level descriptors can be used to tag objects of interest; the tags are then used to construct a high level description

• A search can then be performed against the higher level description schemes

High Definition Video Search Engine

• Applied MPEG-7 to the development of an HD search engine

• Extracted descriptors for approximately 10,000 frames from 2.5 hours of high definition content

• Content provided by the University of Washington from “Visions 05 Cruise”

• Also applied to search for eddies in satellite image collections; super-cells in radar images

Application Architecture

• .NET Windows Forms front end with an embedded Windows Media Player

• SQL Server back-end

• Common Language Run-time Integration for development of stored procedures to manage MPEG-7 XML

• Procedures can be written in .NET languages rather than SQL

Creating a CLR Stored Procedure

CREATE FUNCTION FindUsingVisualDescriptor(@uid int,@token uniqueidentifier, @queryImage varbinary(MAX),@descriptorName nvarchar(256))RETURNS nvarchar(MAX)AS EXTERNAL NAME

MPEG7Document.StoredProcedures.FindUsingVisualDescriptor;

GO

Creating an HTTP EndpointCREATE ENDPOINT MPEG7 STATE = StartedAS HTTP (

SITE = ‘XXX.XXX.XX.XXX', PATH = '/MPEG7Endpoint', AUTHENTICATION = (BASIC), PORTS = (SSL), SSL_PORT = 444 )FOR SOAP (WEBMETHOD 'FindUsingVisualDescriptor' (NAME = 'looking.dbo.FindUsingVisualDescriptor', FORMAT = ALL_RESULTS), …)

User Interface

• UI allows conversion of video into frames using ffmpeg

• Descriptors of choice are then generated for all frames

• Descriptors are persisted to the server

Retrospective Search

• A query image initiates the search

• The descriptor value for the given image is compared with those cached from the video frames or still images

• The top 100 frames that are closest to the query image are returned

Retrospective Search Example

Real-time Event Detection

• In this case, we have a set of known images that have objects of interest

• Descriptors of frames from a real-time stream are compared on a continuous basis with those in the “event library”

• When the difference in descriptor values is below a threshold, an event has been detected

Example of an Event

Reference Event

Use of Multi-Core Systems

• The descriptor extraction process can be made faster by taking advantage of multiple processors or cores

• The total number of frames can be divided up amongst the available processors

• Threads extract the descriptors concurrently to generate chunks of XML

• The threads then signal each other to combine the chunks into a single file with the descriptor XML

Challenges

• Shadows and other lighting issues can create false positives

• May be necessary to use multiple descriptors for classification

• Processing high definition video at 30fps is computationally intensive

• Scaling to a large number of images such as on the web presents a challenge

Conclusion

• MPEG-7 supports a rich framework for content-based searches through its low level descriptors

• Detected content can be tagged effectively using the high level description schemes that can be used to locate, search through and distribute content

Future Directions

• Need to explore ways to speed up descriptor extraction using GPUs or hybrid GPGPUs.

• Explore Cloud Services to implement video services – transcoding video on the fly for different devices, descriptor extraction using HPC clusters, streaming services

• Explore the Surface Computer as a UI

Acknowledgements

• We are thankful to Professor John Delaney from the University of Washington for providing the HD footage

• We are also thankful to the NSF funded LOOKING team for supporting this effort