Feature-Based Object Detection and Tracking: A Systematic Literature Review

Abstract

Correct object detection plays a key role in generating an accurate object tracking result. Feature-based methods have the capability of handling the critical process of extracting features of an object. This paper aims to investigate object tracking using feature-based methods in terms of (1) identifying and analyzing the existing methods; (2) reporting and scrutinizing the evaluation performance matrices and their implementation usage in measuring the effectiveness of object tracking and detection; (3) revealing and investigating the challenges that affect the accuracy performance of identified tracking methods; (4) measuring the effectiveness of identified methods in terms of revealing to what extent the challenges can impact the accuracy and precision performance based on the evaluation performance matrices reported; and (5) presenting the potential future directions for improvement. The review process of this research was conducted based on standard systematic literature review (SLR) guidelines by Kitchenam’s and Charters’. Initially, 157 prospective studies were identified. Through a rigorous study selection strategy, 32 relevant studies were selected to address the listed research questions. Thirty-two methods were identified and analyzed in terms of their aims, introduced improvements, and results achieved, along with presenting a new outlook on the classification of identified methods based on the feature-based method used in detection and tracking process.