Exploring 3 R's of Long-term Tracking: Redetection, Recovery and Reliability

Shyamgopal Karthik, Abhinav Moudgil, Vineet Gandhi; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1011-1020

Abstract


Recent works have proposed several long term tracking benchmarks and highlight the importance of moving towards long-duration tracking to bridge the gap with application requirements. The current evaluation methodologies, however, do not focus on several aspects that are crucial in a long term perspective like Re-detection, Recovery, and Reliability. In this paper, we propose novel evaluation strategies for a more in-depth analysis of trackers from a long-term perspective. More specifically, (a) we test re-detection capability of the trackers in the wild by simulating virtual cuts, (b) we investigate the role of chance in the recovery of tracker after failure and (c) we propose a novel metric allowing visual inference on the ability of a tracker to track contiguously (without any failure) at a given accuracy. We present several original insights derived from an extensive set of quantitative and qualitative experiments.

Related Material


[pdf]
[bibtex]
@InProceedings{Karthik_2020_WACV,
author = {Karthik, Shyamgopal and Moudgil, Abhinav and Gandhi, Vineet},
title = {Exploring 3 R's of Long-term Tracking: Redetection, Recovery and Reliability},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}