Object Tracking: Must-Know Techniques for ML Teams

What Is Object Tracking in Computer Vision?

Object tracking is where you detect a particular object and monitor its position across several frames in a video sequence. It automatically identifies the chosen objects in future frames and what trajectory they follow. This is different to object detection, where you simply find the object.

Object Detection vs. Object Tracking: Key Differences

Object detection identifies the object in a single frame, but doesn’t establish continuity between frames.

Object tracking associates a detected object throughout the whole sequence. Which means that your system knows it’s the same thing in motion. This can save a lot of time when it comes to data annotation in general and video annotation services in particular.

Importance for Machine Learning Workflows

This is important for our ML workflows because it reduces computational costs. The machine learning algorithm can automatically recognize the object in all the frames, so you avoid repetitive detections. Basically, the machine doesn’t have to stop and think about what every part of the picture is.

This becomes important in real-time applications like autonomous vehicles, sports tracking, and security monitoring. For example, the machine could see a pedestrian, track their movements, and realize it has to take evasive action based on their trajectory.

It’s also useful for data annotation services because it can help them automate some of the labelling process. We also see more efficient AI-driven video analytics and robotics.

Key Methods in Object Tracking Explained

Different tracking approaches vary in accuracy, speed, and complexity, making method selection critical for specific applications.

Single Object Tracking (SOT): Precision and Focus

We use SOT when we track a single target in a video. It’s ideal for:

• Security surveillance: Identifying and following an intruder.

• Sports analytics: Tracking a player or ball in a game.

Multiple Object Tracking (MOT): Handling Complexity

MOT allows us to track several objects at the same time. It:

• Handles complex scenarios with overlapping objects.

• Makes it possible for us to use real-time applications such as traffic monitoring or pedestrian tracking.

Fast-moving objects cause motion blur, distorting their shape and position across frames, which disrupts tracking models. Optical flow analyzes pixel movement between frames, estimating motion direction and velocity even when objects are blurred. When combined with CNNs or transformers, it improves real-time tracking accuracy, especially in low-resolution or occluded environments.

Paul DeMott Chief Technology Officer at Helium SEO

How High-Quality Data Improves Object Tracking

Better data means better tracking—high-quality annotations sharpen accuracy, minimize errors, and boost real-world performance.

Why Accurate Data Labeling Matters

You have to annotate data accurately when dealing with all types of LLMs. It’s even more important for image annotation services because if you mislabel the data, you get unreliable tracking. This can lead to:

• Increased retraining cycles

• Lower model performance

• Higher error rates, particularly in occlusion-heavy environments

Labeling Techniques for Better Tracking Accuracy

So, should you label the data yourself, or should you use AI tools? This depends on your budget and the size of your team.

Manual Labeling

You get high accuracy because you have a human checking the work. This also means it’s labor-intensive, which pushes up data annotation pricing.

Automated Labeling

This is faster, but you need to use a high-quality AI model to get reasonable results. You can use datasets like OpenCV object tracking as a good reference point.

Hybrid Approach

You can always combine human expertise with automation for scalable, accurate annotations. Your team members can check that the AI is on the right track.

Don’t have an in-house team? Consider hiring an image annotation company.

Choosing the Right Object Tracking Algorithm

The right algorithm can make or break your tracking system—balancing speed, accuracy, and efficiency is key to success.

Comparing Speed, Accuracy, and Use Cases

Best Algorithms for Beginners

Want a quick and easy start?

• SORT: A simple, easy-to-implement system for video object tracking.

• DeepSORT: Ideal for ML teams using deep learning for tracking.

• YOLOv8: Yolo object tracking is a modern deep-learning tracker for real-time 3D object tracking.

How Does Real-Time Object Tracking Work?

The process has a few essential steps:

1. Object Detection: Identify objects in the first frame.

2. Feature Extraction: Capture object details like shape, color, or deep-learning features.

3. Data Association: Match detected objects across frames.

4. Motion Estimation: Predict object movement based on past frames using object tracking algorithms.

5. Occlusion Handling: Re-identify objects that temporarily disappear.

Real-Time Applications

There are several ways that object detection and tracking algorithms come in handy:

• Self-driving cars: AI object tracking helps detect road signs, pedestrians, and vehicles for safe navigation.

• Drones: Track moving objects like vehicles with an object tracking camera from the air with real-time object tracking.

• Sports tracking: Coaches can use object tracking in video to analyze player movements in real-time to improve performance.

Common Object Tracking Challenges & Solutions

Occlusion, blur, and processing limits challenge tracking, but smart techniques solve them.

Handling Occlusion and Edge Cases

What do you do if an object temporarily disappears behind an obstacle? Say, for example, that a pedestrian steps behind a wall.

You can use Kalman filters or Re-identification models (Re-ID) to recover lost objects. You can also apply DeepSORT to improve tracking accuracy during occlusion. Computer vision object tracking can guess where the object might reappear based on how it’s acted previously as well.

Automating Data Pipelines

One challenge many companies deal with is that labelling the data manually takes a lot of time and is expensive.

Data collection services and annotation companies deal with this by automating at least part of the process. They use automated tools like Roboflow and CVAT or integrate semi-supervised learning to reduce costs.

Optimizing Cost-Effectiveness

A big challenge is that tracking solutions can be expensive. You can get around this by using open-source tools like OpenCV and Norfair. Or you can optimize the dataset size with active learning and, by doing so, reduce annotation costs.

Most trackers lose objects when they leave the frame and re-enter in a different context, making long-term tracking difficult. Deep metric learning helps maintain object identity by embedding features that represent appearance and motion across scenes. A University of Oxford study found it can improve tracking accuracy by up to 25% in challenging scenarios.

Stefan Van der Vlag AI Expert/Founder at Clepher

Top Open-Source Object Tracking Software

Let’s look at the leading open-source object detection and tracking software so you can compare them quickly.

OpenCV

This object tracking computer vision software features traditional and deep-learning tracking methods. It has strong community support and:

• Object Detection

• Face Detection and Recognition

• Feature Extraction and Matching

• Edge Detection

• Video Analysis

• Optical Character Recognition

Norfair

Norfair supports real-time tracking with simple API integration. It’s lightweight and Python-based. There is some community support available, but not to the same extent as with OpenCV. The features include:

• Camera Motion Estimation

• n-Dimensional Tracking

• Re-Identification with Appearance Embeddings

• Predefined Distance Functions

• Fast Inference

• Python 3.8+ Support

Roboflow

Roboflow is a well-rounded option, although it could do more to support object tracking. The community support is good. It provides you with:

• Annotation tools

• Dataset management

• Model deployment.

Industry-Specific Applications of Object Tracking

Now, let’s look at how we can see object tracking in action.

Autonomous Vehicles and Drones

Object tracking ensures that your vehicle or drone can operate safely by detecting other vehicles and obstacles.

Sports Analytics

You can use object tracking to track players and equipment. For example, a baseball coach might use it to track a ball after one of the batters hit it. They could use this to determine who has the best range. You could also use it to track a boxer’s uppercut accuracy.

Security Surveillance

You can use object tracking to monitor crowds, pick out suspicious activities and follow security risks. You can take a big load off human security teams with AI-powered CCTV monitoring. The system can then alert your personnel to risks.

Healthcare Innovations

Object monitoring can be useful in patient monitoring, diagnostic imaging, and clinical research. You could use it to monitor patients for falls in hospitals or frail care facilities. It can also be useful in real-time tumor tracking.

Trends and Innovations (2025 and beyond)

New tracking methods, from transformers to zero-shot learning, are reshaping the future of AI vision.

Transformer-Based Object Trackers

CoTracker and DETR-based models will leverage transformers for improved object tracking. These advances should reduce occlusion errors and improve real-time performance.

Rise of Zero-shot and Few-shot Learning

We’ll see object tracking algorithms that are without needing extensive labeled data. This will accelerate deployment into new domains.

Getting Started: Practical Tools and Implementation

The right tools and frameworks simplify tracking implementation, making it easier to build and optimize models.

Implementing YOLOv8 for Object Tracking

• You train your TOLOv8 on a custom dataset

• You then fine-tune the tracking parameters for optimal performance

• Then it’s time to deploy the model in real-time applications

Quick Guide to DeepSORT Setup

• Install the required dependencies (pip install deep_sort.)

• Configure using pre-trained Re-ID models

• Integrate with YOLO for real-time tracking

• Monitor the model’s performance

Integrating YOLOv8 and DeepSORT

You’ll:

1. Detect objects with YOLOv8

2. Assign unique IDs with DeepSORT

3. Continuously track objects across frames

Check this paper where this combo excels in real-time tracking.

Tailored Recommendations for Your Role

• Business Managers: Vet vendors to ensure a strong return on investment.

• ML Engineers: Use OpenCV, automate labeling, or outsource to streamline tracking.

• Academic Researchers: Ensure dataset quality for peer-reviewed studies.

About Label Your Data

If you choose to delegate data annotation, run a free data pilot with Label Your Data. Our outsourcing strategy has helped many companies scale their ML projects. Here’s why:

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FAQ

What is object tracking?

Object tracking follows an object across video frames in real time. It helps AI understand movement and keep track of objects even if they briefly disappear. This is useful in self-driving cars, surveillance, and sports analysis.

What is the difference between motion tracking and object tracking?

Motion tracking looks at overall movement patterns without identifying specific objects. It is used in video stabilization and animation. Object tracking follows one object across frames, ignoring the background and other movements. This is key in applications like security cameras and traffic monitoring.

What is an example of object detection?

YOLOv8 detecting pedestrians in a self-driving car is one example. It spots and labels people in a frame, helping the car recognize them. Unlike object tracking, it does not follow them across multiple frames.

Written by

Karyna Naminas CEO of Label Your Data

Karyna is the CEO of Label Your Data, a company specializing in data labeling solutions for machine learning projects. With a strong background in machine learning, she frequently collaborates with editors to share her expertise through articles, whitepapers, and presentations.