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Mastering OpenCV Object Tracking: A Comprehensive Guide for Beginners and Experts

Liza Brown
Liza Brown Originally published Sep 05, 22, updated Mar 27, 24

OpenCV Tracking is a popular method used for tracking objects in a video in real-time. OpenCV is a powerful library used for image processing and computer vision applications.

From real-time video analysis to Augmented Reality, OpenCV Tracking has a wide range of uses and can help both private and public sectors to detect and track the desired object in an image or video.

In this comprehensive guide, we’ll explore OpenCV Tracking and its pre-built trackers. We will use the OpenCV library and Python to implement this Object Tracking application.

In this article
    1. SOT & MOT
    2. The Difference Between Tracking And Detection
    1. Image Tracking
    2. Video Tracking
  1. What Are The Difficulties Of Object Tracking
    1. Stages Of The Object Tracking Process
    2. Methods Of Object Tracking
    1. What Is OpenCV
    2. The Pre-Built Algorithms OpenCV Offers For Object Tracking
    3. Implementation In Python And OpenCV

Part 1. What Is Object Tracking?

Object Tracking determines the location of an object in a video and can be used for a variety of purposes, including surveillance, security, traffic control, digital forensics, anomaly detection, gesture recognition, visual surveillance, human-computer interaction, augmented reality, and robot vision.

Even though it may seem easy for an average person, object tracking is quite complex for computers because they have to analyze a set of frames and estimate the object’s trajectory based on numbers instead of images.

Because of how complicated it is, Object Tracking is considered one of the most difficult tasks in computer vision. Even though it's a challenge, it's not impossible to accomplish.

And there are two levels of Object Tracking that you should learn about:

1.     SOT & MOT

  1. Single Object Tracking (SOT): SOT is a tracking algorithm that creates advanced appearance models and/or motion models to manage difficult issues like out-of-plane rotations, illumination variations, or scale changes.
  2. Multiple Object Tracking (MOT): MOT tracks every part of a video and is mainly concerned with mid-level tasks and serves as a foundation for high-level tasks.

2.     The Difference Between Tracking And Detection

After knowing what Object Tracking is, there is another concept you should also be aware of - Object Detection.

The Object Tracking and Object Detection are two important methods in computer vision. Object Tracking tracks the position of a given object in an image in real-time, while Object Detection identifies and localizes objects in an image or video. That is the difference.

Part 2. Types Of Object Tracking

Image Tracking

Image tracking allows users to interact with digital content more naturally and helps users move and interact with digital objects in the same manner as they will with real-world physical objects.

Image tracking can be used in various augmented reality (AR) applications, such as AR gaming, shopping, and navigation. Moreover, you can also use it for educational purposes, such as teaching anatomy or providing instructions for a difficult task.

Video Tracking

Video tracking is the process of tracking objects in a video sequence and can be used for various applications, such as detecting and counting objects in a scene, understanding their motions, and recognizing activities.

Part 3. What Are The Difficulties Of Object Tracking?

As what has been mentioned above, the use of object tracking is versatile. However, there are some challenges associated with Object Tracking, such as:

Background Clutter: The background can impact extracting an object's features. If the background is more densely populated, detecting or tracking the object of interest can be more difficult.

Occlusion: This is a phenomenon where the object being tracked is affected by the background or foreground. This causes the tracking algorithm to get confused due to multiple objects coming close together, hence, losing track of the object.

Varying spatial scales: Varying spatial scales refer to the target object being of a different size or shape, which can confuse the algorithm, leading to errors.

Training and Tracking Speed: If the object is moving too fast or is too small, it can be difficult to track. Therefore, the tracking module has to be designed to track, identify, and localize objects in a matter of seconds.

Part 4. Stages Of The Object Tracking Process

After the introduction of object tracking technically, let’s learn about its mechanism of it from two sides: periods of the working process and the way it tracks.

1.     Stages Of The Object Tracking Process

Following are the stages of the Object Tracking process:

Target Initialization

Target initialization is the first step in Object Tracking and involves defining the targets or objects of interest and drawing a bounding box around them in the initial frame. After that, the tracker has to detect the position of the object in the frame while drawing the bounding box.

Appearance Modeling

Appearance modeling is important for understanding how an object will look under different circumstances. If the object being tracked changes appearance due to different lighting, angle, or speed, the tracking algorithm may lose its information and the object itself. Therefore, it has to be conducted to let the algorithm detect the changes and distortion.

Motion Estimation

Motion estimation entails the capacity of the model to anticipate an object's future position with precision.

Target Positioning

Motion estimation is a process of approximating the most likely region where an object could be present. After the object's location is approximated, a visual model can be used to determine the target's location more accurately.

2.     Methods Of Object Tracking

Here’re some methods of Object Tracking:

OpenCV-Based Object Tracking

OpenCV Object Tracking method is widely used because it offers several built-in functions designed specifically for this purpose, such as GOTURN, MIL, MediandFlow, and CSRT. The tracker type you select will depend on your specific application design. Each tracker has its pros and cons, so there is not a single type of tracker ideal for all applications.


Multi-Domain Convolutional Neural Network Tracker is a convolutional neural-based visual tracker and has won the VOT2015 challenge. Moreover, it is composed of domain-specific layers, branches, and multiple shared layers.

The MDNetT tracker is a deep neural network that is trained on numerous Object Tracking datasets. The tracker is designed to work in multiple domains, including video, text, and images.


DeepSort algorithm can track objects in real-time and uses YOLO v3 to compute the bounding boxes around the objects in a frame.

DeepSort uses Kalman filter from Simple Online and Realtime Tracking algorithm and an identification model, ReID to interlink bounding boxes and object tracks.


ROLO is a novel Object Tracking method that uses recurrent neural networks (RNNs) to learn object-specific Spatio-temporal representations. This method works with YOLO, an object detection algorithm, and an LTSM for determining the trajectory of an object.

Part 5. The powerful backup: OpenCV

We have demonstrated how powerful OpenCV Tracking is and how it works. However, do you wonder what OpenCV is? How it supports the tracking goals being achieved? In this part, we will focus on OpenCV and help you better understand the the underlying mechanisms.

1.     What Is OpenCV?

OpenCV is a powerful library that allows you to perform image processing, deep learning, machine learning, and computer vision tasks on live video feeds. Moreover, it can be used to detect objects, track their movements, and recognize faces.

It can help process the data that have videos and images and has been used in various applications, such as object recognition and detection, autonomous cars, robots, medical image analysis, automated surveillance, and anomaly detection.

2.     The Pre-Built Algorithms OpenCV Offers For Object Tracking

There are 8 pre-built algorithms that OpenCV offers for Objects Tracking. All of them have their uniquility and here we will present their pros and cons for your better understadning.

Tracker Pros Cons
  • Track objects accurately.
  • Relatively slow.
  • Unable to stop when an object is lost.
MIL Tracker
  • Strong resistance to noise.
  • Gives accurate results.
  • Low speed.
  • Unable to stop tracking once the object is lost.
KCF Tracker
  • High speed and accuracy.
  • Stops tracking when the object is lost.
  • Inability to continue tracking objects once lost.
TLD Tracker
  • Resistance to overlapping and object scaling.
  • Unstable in object detection and tracking.
  • Constantly loses object.
  • High accuracy and speed.
  • Accurate in determining the object’s loss.
  • Loses object quickly due to high speed.
GOTURN Tracker
  • Shows resistance to obstructions and noise.
  • If the speed of an object is high, it shifts to another object.
MOSSE Tracker
  • Able to continue tracking after the object is lost.
  • High speed.
  • Loses object easily if very high speed.
CSRT Tracker
  • Good resistance to overlapping.
  • High accuracy.
  • Low speed.
  • Unstable when an object is lost.

3.     Implementation In Python And OpenCV

Now, after theoretical explanation of OpenCV and OpenCV Tracking, let’s learn Object Tracking OpenCV Python with coding and our simple and step-by-step instructions:

Install The Dependencies

Install the pycharm app on your PC, and launch it. Create a new project and go to Terminal. Install the opencv-contrib-python package by pasting the following command into the Terminal.

Set Up the Trackers

Now, create a Jupyter-notebook and set up the trackers with this code:


  • cv2. version returns the OpenCV library version number.
  • Save the name of the eight trackers.
  • Check the list for the version you are working on and create a tracker object based on the number.

Capture The Video Input

For capturing the video input, write the following lines of code:


  • Use the VideoCapture class to capture a video via the webcam or get a saved one from your PC.
  • In VideoCapture - line 2, give the path to your video.
  • Comment on the second line to use the webcam for tracking and uncomment the third one.

Creating The Bounding Box And Initializing The Tracker

Define an initial random bounding box or select the choices for the object you want to track with the following code:

Start The Tracker And Check The Output

Finally, write the below code lines to start the tracker:


  • Read each video frame.
  • Start the timer and, through the tracker, determine the trajectory of the object.
  • Use the trajectory to draw the bounding box.
  • The program will start tracking. Afterward, press the space bar to stop tracking.

Video – Object Tracking with Opencv and Python


In this article, we’ve discussed Object Tracking in detail and how it compares with Object Detection. Furthermore, we explored the methods used for tracking objects and provided complete guidance on the OpenCV Tracking method and its built-in functionalities.

Hopefully, you find this guide helpful, and you can now track objects in real-time without any issues.

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Liza Brown
Liza Brown Mar 27, 24
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