Hi Guest, 30 September 2020 Wednesday IST

About CUSAT | About Department | Alumni | Sitemap | Disclaimer  

     
 
  Home > Academic/Programmes > Programme Structure > CSE (2019)
       
       
 
19-475-0403 REAL-TIME VIDEO ANALYTICS
Core/Elective: Elective Semester: 4 Credits: 4
Course Description

This course is about video analytics enabling automated analysis of detection of interesting spatial and temporal events. Image and video analysis include techniques capable of extracting high-level information from the data. Starting from the foundations of image / video analysis this course covers algorithms applied in systems for video analytics so as to develop interesting applications including surveillance

Course Objectives

To gain a working knowledge with imgae and video processing
To understand the analytics on video
To apply the knowledge to develop applications that use video analytics

Course Content

Module I:
Fundamentals: Image feature extraction: Feature point detection, Scale Invariant Feature Transform, Edge Detection, Color features. Multiple View Geometry: Perspective Projection Camera Model, Epipolar Geometry, Probabilistic inference, Pattern recognition and Machine learning: SVM and AdaBoost. Background Modeling and Subtraction: Kernel Density Approximation, Background Modeling and Subtraction Algorithms

Module II
Object Detection and Tracking: Pedestrian detection by boosting local shape features: Tree learning algorithms, Edgelet features. Occluded pedestrian detection by part combination. Pedestrian tracking by Associating Detection Responses. Vehicle Tracking and Recognition: Joint tracking and Recognition framework, Joint appearance-motion generative model, Inference algorithm for joint tracking and recognition

Module III
Human Motion Tracking: Image feature representation, Dimension reduction and Movement dynamics learning. Human action recognition: Discriminative Gaussian Process dynamic model. Human Interaction recognition: Learning human activity, Track-body Synergy framework. Multi-camera calibration and global trajectory fusion: Non-overlapping and overlapping cameras. Applications: Attribute-based people search, Soft biometrics for video surveillance: Age estimation from face, Gender recognition from face and body

Module IV
Face Recognition and Gait Analysis: 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, Factorial HMM and Parallel HMM for Gait Recognition, Face Recognition Performance

Module V
Behavioral Analysis and Activity Recognition: Event Modeling- Behavioral Analysis- Human Activity Recognition-Complex Activity Recognition- Activity modeling using 3D shape, Video summarization, Shape based activity models, Suspicious Activity Detection. Video Segmentation and Key Frame Extraction: Introduction, Applications of Video Segmentation, Shot Boundary Detection, Pixel-based Approaches, Block-based Approaches, Histogram-based Approaches, Clustering-based Approaches, Performance Measures, Shot Boundary Detection, Key-frame Extraction

REFERNCES

1. Francesco Camastra, Alessandro Vinciarelli, "Machine Learning for Audio, Image and Video Analysis",Springer Nature, Second Edition, 2015.
2. Yunqian Ma, Gang Qian, “Intelligent Video Surveillance: Systems and Technology”, CRC Press, First Edition, 2009.
3. Fredrik Nilsson, Communications Axis, “Intelligent Network Video: Understanding Modern Video Surveillance Systems”, CRC Press, Second Edition, 2017.
4. Anthony C. Caputo, “Digital Video Surveillance and Security”, Butterworth-Heinemann, Second Edition, 2014.
5. Herman Kruegle, “CCTV Surveillance: Video Practices and Technology”,Butterworth-Heinemann, Second Edition, 2006.
6. Amit K.Roy-Chowdhury, Rama Chellappa, S. Kevin Zhou, Al Bovik, “Recognition of Humans and Their Activities Using Video (Synthesis Lectures on Image, Video, and Multimedia Processing)”, Taxmann Publications Private Limited, 2005.
7. Richard Szeliski, "Computer Vision: Algorithms and Applications", Springer, First Edition, 2010.
8. David A. Forsyth, Jean Ponce, "Computer Vision- A Modern Approach", Pearson Education, Second Edition, 2015.


Copyright © 2009-20 Department of Computer Science,CUSAT
Design,Hosted and Maintained by Department of Computer Science
Cochin University of Science & Technology
Cochin-682022, Kerala, India
E-mail: csdir@cusat.ac.in
Phone: +91-484-2577126
Fax: +91-484-2576368