Visual Tracking: An Experimental Survey

TLDR

Object tracking remains a challenging, active research area in computer vision, with many trackers proposed over two decades yet evaluated on limited datasets, despite needing robust performance across varied conditions such as illumination changes, occlusion, clutter, camera motion, low contrast, and specularities. The study systematically evaluates nineteen diverse trackers on 315 video fragments spanning these challenging conditions, using publicly available implementations from 2010–2011. Trackers can be objectively evaluated using survival curves, Kaplan–Meier statistics, and Grubbs tests; the F‑score is as effective as the object tracking accuracy score, and the analysis reveals clear strengths and weaknesses across conditions.

Abstract

There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is a difficult problem, therefore, it remains a most active area of research in computer vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities, and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers.