Visual Odometry Based Omni-directional Hyperlapse
Published in Proceedings of National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2017),, 2017
Recommended citation: P. Rani, A. Jangid, V.P. Namboodiri and K.S. Venkatesh, “Visual Odometry based Omni-directional Hyperlapse”, Proceedings of National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2017), Mandi, India 2017 http://vinaypn.github.io/files/ncvpripg2017.pdf
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
Recommended citation: P. Rani, A. Jangid, V.P. Namboodiri and K.S. Venkatesh, “Visual Odometry based Omni-directional Hyperlapse”, Proceedings of National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2017), Mandi, India 2017