# Automatic Tracker Selection w.r.t Object Detection Performance

Source: [Automatic Tracker Selection w.r.t Object Detection Performance](<https://www.sota2.com/research/paper/68fa614f1e209131ce9f3ae6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:56:35.880Z

Authors: Duc Phu Chau, Fran\\c{c}ois Bremond, Monique Thonnat, Slawomir Bak

Published: 2014-04-08T04:09:32.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1404.2005>)

[PDF](<https://arxiv.org/pdf/1404.2005.pdf>)