Visual odometry based omni-directional hyperlapse

Prachi Rani, Arpit Jangid, Vinay P. Namboodiri, K. S. Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The prohibitive amounts of time required to review the large amounts of data captured by surveillance and other cameras has brought into question the very utility of large scale video logging. Yet, one recognizes that such logging and analysis are indispensable to security applications. The only way out of this paradox is to devise expedited browsing, by the creation of hyperlapse. We address the hyperlapse problem for the very challenging category of intensive egomotion which makes the hyperlapse highly jerky. We propose an economical approach for trajectory estimation based on Visual Odometry and implement cost functions to penalize pose and path deviations. Also, this is implemented on data taken by omni-directional camera, so that the viewer can opt to observe any direction while browsing. This requires many innovations, including handling the massive radial distortions and implementing scene stabilization that need to be operated upon the least distorted region of the omni view.

Original languageEnglish
Title of host publicationComputer Vision, Pattern Recognition, Image Processing, and Graphics - 6th National Conference, NCVPRIPG 2017, Revised Selected Papers
EditorsChetan Arora, Renu Rameshan, Sumantra Dutta Roy
PublisherSpringer Verlag
Pages3-13
Number of pages11
ISBN (Print)9789811300196
DOIs
Publication statusPublished - 1 Jan 2018
Event6th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2017 - Mandi, India
Duration: 16 Dec 201719 Dec 2017

Publication series

NameCommunications in Computer and Information Science
Volume841
ISSN (Print)1865-0929

Conference

Conference6th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2017
CountryIndia
CityMandi
Period16/12/1719/12/17

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Cite this

Rani, P., Jangid, A., Namboodiri, V. P., & Venkatesh, K. S. (2018). Visual odometry based omni-directional hyperlapse. In C. Arora, R. Rameshan, & S. Dutta Roy (Eds.), Computer Vision, Pattern Recognition, Image Processing, and Graphics - 6th National Conference, NCVPRIPG 2017, Revised Selected Papers (pp. 3-13). (Communications in Computer and Information Science; Vol. 841). Springer Verlag. https://doi.org/10.1007/978-981-13-0020-2_1