Page 94 - R2017-REC-ECE-UG Syllabus

Department of ECE, REC

UNIT I INTRODUCTION TO BIG DATA AND DATA ANALYSIS 9
Introduction to Big Data Platform – Challenges of Conventional Systems – Web Data – Evolution of Analytic
Scalability – Analytic Processes and Tools – Analysis Vs Reporting – Modern Data Analytic Tools – Data
Analysis: Regression Modeling – Bayesian Modeling – Rule Induction.

UNIT II MINING DATA STREAMS 9
Introduction to Stream Concepts – Stream Data Model And Architecture – Stream Computing –Sampling
Data in a Stream – Filtering Streams – Counting Distinct Elements in a Stream– Estimating Moments –
Counting Oneness in a Window – Decaying Window – Real Time Analytics Platform (RTAP) Applications –
Case Studies.

UNIT III VIDEO ANALYTICS 9
Introduction – Video Basics – Fundamentals for Video Surveillance – Scene Artifacts – Object Detection and
Tracking: Adaptive Background Modelling and Subtraction – Pedestrian Detection and Tracking – Vehicle
Detection and Tracking – Articulated Human Motion Tracking in Low Dimensional Latent Spaces.

UNIT IV BEHAVIOURAL ANALYSIS AND ACTIVITY RECOGNITION 9
Event Modelling – Behavioural Analysis – Human Activity Recognition – Complex Activity Recognition –
Activity modeling using 3D shape – Video summarization – shape based activitymodels – Suspicious Activity
Detection.

UNIT V HUMAN FACE RECOGNITION AND GAIT ANALYSIS 9
Introduction: Overview of Recognition algorithms – Human Recognition using Face: - Face
Recognition from still images – Face Recognition from video – Evaluation of Face Recognition Technologies
– Human Recognition using gait: HMM Framework for Gait Recognition – View Invariant Gait Recognition –
Role of Shape and Dynamics in Gait Recognition.

TOTAL=45 PERIODS

OUTCOMES:
Upon successful completion of this course, students will be able to:
• Work with big data platform and its analysis techniques.
• Design efficient algorithms for mining the data from large volumes.
• Work with surveillance videos for analytics.
• Design optimization algorithms for better analysis and recognition of objects in a scene.
• Model a framework for Human Activity Recognition.

TEXT BOOKS
1.Michael Berthold, David J.Hand, “Intelligent Data Analysis”, Springer, 2007.
2.Anand Rajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”, Cambridge, University Press,
2012.

REFERENCES
1. Yunqian Ma, Gang Qian, “Intelligent Video Surveillance: Systems and Technology”, CRC
Press (Taylor and Francis Group), 2009.
2.Rama Chellappa, Amit K.Roy– Chowdhury, Kevin Zhou.S, “Recognition of Humans and their Activities
using Video”, Morgan & Claypool Publishers, 2005.

Curriculum and Syllabus | B.E. Electronics and Communication Engineering | R2017 Page 94