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Visual Tracking: An Experimental Survey.

Arnold W M Smeulders, Dung M Chu, Rita Cucchiara

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 10, 2015

Summary

This summary is machine-generated.

This study systematically evaluates 19 object trackers on 315 video fragments, revealing performance differences under challenging conditions. Objective evaluation methods show the F-score is effective for assessing object tracking accuracy (OTA).

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Area of Science:

Computer Vision
Machine Learning

Background:

Object tracking is a challenging computer vision problem with numerous proposed algorithms.
Existing tracker evaluations often use limited datasets, hindering comprehensive performance assessment.

Purpose of the Study:

To systematically evaluate nineteen object trackers on a diverse dataset of 315 video fragments.
To provide an objective analysis of tracker strengths and weaknesses under various challenging conditions.

Main Methods:

Experimental evaluation of nineteen selected object trackers.
Utilized 315 video fragments encompassing illumination changes, occlusion, clutter, and camera motion.
Employed survival curves, Kaplan Meier statistics, and Grubs testing for objective assessment.

Main Results:

Demonstrated the effectiveness of survival curves and statistical methods for objective tracker evaluation.
Found the F-score to be as effective as object tracking accuracy (OTA) for performance measurement.
Identified specific strengths and weaknesses of various trackers across diverse scenarios.

Conclusions:

Systematic, large-scale evaluation provides crucial insights into object tracker performance.
Objective metrics like survival curves and F-score are vital for reliable tracker assessment.
This work offers a benchmark for future object tracking research and development.