Apr 30, 2014 · Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.

C++ KCF Tracker This package includes a C++ class with several tracking methods based on the Kernelized Correlation Filter (KCF) [1, 2]. It also includes an executable to interface with the VOT benchmark.

May 30, 2021 · BallTrackr is an advanced video application designed to detect and track a soccer ball in real-time using YOLOV3 and KCF Tracker. Object tracking with OpenCV using KCF tracker. Add a description, image, and links to the kcf-tracker topic page so that developers can more easily learn about it.

Try different options (hog/gray, fixed/flexible window, singlescale/multiscale) of KCF tracker by modifying the arguments in line tracker = kcftracker.KCFTracker(False, True, False) # hog, fixed_window, multiscale in run.py.

Jan 8, 2013 · KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed. This tracking method is an implementation of [101] which is extended to KCF with color-names features ([51]).

Parallel KCF Tracking. A 15-618 Final Project by Ilaï Deutel and Denis Merigoux. Summary. We modified the OpenCV implementation of the KCF object tracking algorithm to use the NVIDIA GPUs of the GHC machines. Our 1.84x final speedup obtained on a fullHD video increased the number of FPS from 8.4 to 12.8. Background Tracking API

Kernelized Correlation Filter (KCF) is one of the recent finding which has shown good results. Based on the idea of traditional correlational filter, it uses kernel trick and circulant matrices to significantly improve the computation speed.

Feb 14, 2018 · Working on latest version of OpenCV on Python to create a program that tracks specific objects as they move around in front of the camera. the ultimate goal being just to count how many of that specific object when its in the bottom half of the frame.

Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.

In this paper, an improved KCF algorithm is proposed. The algorithm adds the reliability estimation and re detection mechanism to solve the problem of severe occlusion and rapid movement of the target. The scale pool method is introduced.