Lecture videos have been commonly used to supplement in-class teaching and for distance learning. Videos recorded during in-class teaching and made accessible online are a versatile resource on par with a textbook and the classroom itself. Nonetheless, the adoption of lecture videos has been limited, in large part due to the difficulty of quickly accessing the content of interest in a long video lecture. In this work, we present “video indexing” and “keyword search” that facilitate access to video content and enhances user experience. Video indexing divides a video lecture into segments indicating different topics by identifying scene changes based on the analysis of the difference image from a pair of video frames. We propose an efficient indexing algorithm that leverages the unique features of lecture videos. Binary search with frame sampling is employed to efficiently analyze long videos. Keyword search identifies video segments that match a particular keyword. Since text in a video frame often contains a diversity of colors, font sizes and backgrounds, our text detection approach requires specialized preprocessing followed by the use of off-the-shelf OCR engines, which are designed primarily for scanned documents. We present image enhancements: text segmentation and inversion, to increase detection accuracy of OCR tools. Experimental results on a suite of diverse video lectures were used to validate the methods developed in this work. Average processing time for a one-hour lecture is around 14 minutes on a typical desktop. Search accuracy of three distinct OCR engines - Tesseract, GOCR and MODI increased significantly with our preprocessing transformations, yielding an overall combined accuracy of 97%. The work presented here is part of a video streaming framework deployed at multiple campuses serving hundreds of lecture videos.
[1]
Thomas D. C. Little,et al.
A Survey of Technologies for Parsing and Indexing Digital Video1
,
1996,
J. Vis. Commun. Image Represent..
[2]
Lecia Jane Barker,et al.
Development and evaluation of indexed captioned searchable videos for STEM coursework
,
2012,
SIGCSE '12.
[3]
Venkat Subramaniam,et al.
Tablet PC video based hybrid coursework in computer science: report from a pilot project
,
2007,
SIGCSE '07.
[4]
David H. C. Du,et al.
Video-based hypermedia for education-on-demand
,
1997,
MULTIMEDIA '96.
[5]
Bernd Girod,et al.
Mobile interactive region-of-interest video streaming with crowd-driven prefetching
,
2011,
IMMPD '11.
[6]
Wolfgang Effelsberg,et al.
Automatic text segmentation and text recognition for video indexing
,
2000,
Multimedia Systems.
[7]
Jean-Michel Morel,et al.
Image Interpolation
,
1998
.
[8]
SubhlokJaspal,et al.
Tablet PC video based hybrid coursework in computer science
,
2007
.
[9]
Qi Tian,et al.
Fast and robust short video clip search using an index structure
,
2004,
MIR '04.
[10]
John R. Kender,et al.
Semantic keyword extraction via adaptive text binarization of unstructured unsourced video
,
2009,
2009 16th IEEE International Conference on Image Processing (ICIP).
[11]
Michael Bianchi.
Automatic video production of lectures using an intelligent and aware environment
,
2004,
MUM '04.