Spatio-temporal Good Features to Track

Christoph Feichtenhofer, Axel Pinz; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2013, pp. 246-253


This paper presents two fundamental contributions that can be very useful for any autonomous system that requires point correspondences for visual odometry. First, the Spatio-Temporal Monitor (STM) is an efficient method to identify good features to track by monitoring their spatiotemporal (x-y-t) appearance without any assumptions about motion or geometry. The STM may be used with any spatial (x-y) descriptor, but it performs best when combined with our second contribution, the Histogram of Oriented Magnitudes (HOM) descriptor, which is based on spatially oriented multiscale filter magnitudes. To fulfil the real-time requirements of autonomous applications, the same descriptor can be used for both, track generation and monitoring, to identify low-quality feature tracks at virtually no additional computational cost. Our extensive experimental validation on a challenging public dataset demonstrates the excellent performance of STM and HOM, where we significantly outperform the well known "Good Features to Track" method and show that our proposed feature quality measure highly correlates with the accuracy in structure and motion estimation.

Related Material

author = {Christoph Feichtenhofer and Axel Pinz},
title = {Spatio-temporal Good Features to Track},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {June},
year = {2013}