From this page,you can install our detection agent and start the detection of your configuration. If you visit this page for the first time,simply click on "Install". Ma-Config Télécharger - Ma-Config (Ma-Config) Analyse le PC à fond et du producteur et cliquer sur Start the detection (commencer la détection). TLD: Tracking-Learning-Detection. TLD is part of the Visual Object Tracking Repository, which aims at providing a central repository for state-of-the-art tracking.

Format: Fichier D’archive
Version: Dernière
Licence: Usage Personnel Seulement
Système d’exploitation: Android. iOS. MacOS. Windows XP/7/10.
Taille: 22.29 Megabytes

The operator provides additional bounding box and this information is smoothly integrated into the target model. The object of interest is defined by a bounding box in a single frame. Documentation Documentation is available on Read the Docs. Some features may not work without JavaScript. Thanks to Marco Giuliani for preparing Italian version of help! The browser may ask you to confirm the action: At the end of this process,a web page will automatically open to display the results.

What's TLD? This algorithm, also known as “Predator” algorithm, developed by Zdenek Kalal. For more information, please visit his homepage. It is most appropriate for real-time applications with limited compute power that wish to track all kids of objects and ensure re-detection capabilities. TLD2 is a. We propose a novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection.


The object of interest is defined by a bounding box in a single frame. TLD simultaneously tracks the object, learns its appearance and detects it whenever it appears in the video. Skip to content.

Dismiss Join GitHub today GitHub is home to over 36 million developers working together to host and review code, manage projects, and build software together.

Sign up.

Detect my configuration (Version :

No description, website, or topics provided. Find File. Download ZIP.

Sign in Sign up. Launching GitHub Desktop Go back.


Launching Xcode Launching Visual Studio Latest commit 1d90cce Apr 4, This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame.

Tracking-Learning-Detection - IEEE Journals & Magazine

In every frame that follows, the task is to determine the object's location and extent or indicate that the object is not present. We propose a novel tracking framework TLD that explicitly decomposes the long-term tracking task into tracking, learning, and detection. The tracker follows the object from frame to frame.

The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates the detector's errors and updates it to avoid these errors in the future. We study how to identify the detector's errors and learn from them.


The learning process is modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found.

We carry out an extensive quantitative evaluation which shows a significant improvement over state-of-the-art approaches. Published in: Date of Publication: