Automatic Tracker Selection w.r.t Object Detection Performance

TL;DR

This paper introduces an adaptive multi-object tracking method that dynamically selects the best tracker based on video content, improving performance by combining detection enhancement and tracker selection.

Contribution

It proposes a novel approach that combines object detection improvement with online tracker selection tailored to video content variations.

Findings

Outperforms recent state-of-the-art trackers on public datasets.

Uses KLT feature tracking to enhance detection.

Employs online evaluation for tracker selection.

Abstract

The tracking algorithm performance depends on video content. This paper presents a new multi-object tracking approach which is able to cope with video content variations. First the object detection is improved using Kanade- Lucas-Tomasi (KLT) feature tracking. Second, for each mobile object, an appropriate tracker is selected among a KLT-based tracker and a discriminative appearance-based tracker. This selection is supported by an online tracking evaluation. The approach has been experimented on three public video datasets. The experimental results show a better performance of the proposed approach compared to recent state of the art trackers.