Tracking Methods in Machine Learning: A Complete Guide

Tracking methods in machine learning refer to the techniques used to monitor, follow, and predict the state or position of objects, data points, or model behavior over time. Whether you're building a self-driving car system, a sports analytics tool, or a fraud detection pipeline, tracking is what ties observations across time into something meaningful.

Machine learning models don't stop evolving after they're trained. Their performance can change as new data arrives, user behavior shifts, or business requirements grow. That's why tracking methods in machine learning have become a core part of building reliable AI systems. They help data teams monitor experiments, datasets, models, and predictions throughout the machine learning lifecycle.

This blog covers the most widely used tracking methods in machine learning, how each one works, where they're actually applied, and what trade-offs you should know before picking one for your project.

Explore upGrad's Machine learning programs to build practical machine learning skills. Learn computer vision, deep learning, MLOps, and model deployment through hands-on projects that cover real-world AI applications, including object tracking and intelligent systems.

What Does Tracking Mean in Machine Learning?

Tracking, at its core, is about continuity. You're not just detecting something once. You're following it across frames, timesteps, or data streams and answering the question: is this the same object I saw before?

In ML, this gets complicated fast. Objects move. They get occluded. Sensors introduce noise. Two objects can look identical. These aren't edge cases; they happen constantly in real-world deployments.

Tracking methods solve this by combining detection (finding something) with association (linking that detection to a past observation). The method you use determines how well your system handles each of those challenges.

Categories of Tracking Methods

There are two broad categories worth knowing:

Tracking Method	Description	Best Use Cases
Model-based Tracking	Uses a mathematical model to predict an object's next position or state.	Autonomous vehicles, robotics, motion prediction, and video surveillance with continuous object movement.
Detection-based Tracking	Detects objects in each frame and matches them across frames to track movement.	Multi-object tracking, pedestrian tracking, sports analytics, traffic monitoring, and real-time video analysis.

Do read: Machine Learning Methods: A Complete Beginner's Guide

 

Kalman Filter: The Classic Approach

The Kalman filter is one of the oldest and most widely used tracking methods in machine learning and signal processing. It was developed in the 1960s, but it's still the backbone of many modern tracking pipelines.

Here's why it works. The filter maintains two things: a predicted state (where the object is expected to be) and an uncertainty estimate (how confident the model is). Every time a new observation comes in, the filter updates both.

It's fast, interpretable, and works well when object motion is roughly linear. Think vehicle tracking on a highway or trajectory prediction in robotics.

Where it struggles

It struggles in:

• Non-linear motion (sharp turns, erratic movement)
• High occlusion scenarios
• Multi-object scenes without additional assignment logic

The Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are variants built to handle non-linear systems. They're more computationally expensive, but they're worth it when motion patterns are complex.

Variant	Best For	Trade-off
Kalman Filter	Linear motion, low noise	Breaks down with non-linear paths
EKF	Mild non-linearity	Approximation errors at high curvature
UKF	Strong non-linearity	Higher compute cost

Must read: Labeled Data in Machine Learning: What It Is and Why It Matters 

SORT and DeepSORT: Tracking Multiple Objects at Once

Single-object tracking is the easier problem. Multi-object tracking (MOT) is where things get genuinely hard. You have to detect multiple objects, assign consistent IDs to each, and handle cases where they disappear and reappear.

SORT (Simple Online and Realtime Tracking) combines the Kalman filter with the Hungarian algorithm. It predicts where each tracked object will be in the next frame, runs a detector, then uses Hungarian assignment to match predictions to detections.

It's really fast. But it doesn't use any appearance information, which means it fails badly when objects cross paths or look similar.

DeepSORT fixes this by adding a deep learning component. It extracts appearance features from a CNN and uses them alongside motion cues to make better associations. The result is a system that can re-identify objects even after they've been occluded.

Key differences at a glance

Why this matters practically: if you're building a pedestrian tracking system for a retail store, SORT might confuse two people wearing similar clothes. DeepSORT handles that much better.

DeepSORT	SORT
DeepSORT uses position, velocity, and appearance features	SORT uses only position and velocity for matching
DeepSORT performs better in crowded scenes	SORT is faster and simpler to deploy

 

Explore upGrad's Master of Science in Machine Learning & AI from Liverpool John Moores University to build advanced expertise in AI, machine learning, computer vision, deep learning, MLOps, and generative AI. 

Particle Filters: Handling Uncertainty Better

The particle filter (also called Sequential Monte Carlo) takes a fundamentally different approach. Instead of representing state as a single estimate with uncertainty, it represents state as a set of particles, where each particle is a possible state.

More particles in one region means the model thinks the object is more likely to be there. As new observations arrive, particles are reweighted and resampled. The result is a distribution over possible states rather than a single point estimate.

This makes particle filters much better than Kalman filters when:

• The motion model is highly non-linear
• The observation noise isn't Gaussian
• You're tracking in conditions where the object can disappear behind obstacles

The catch? They're computationally expensive. The more particles you use, the better the estimate, but also the higher the cost. For real-time tracking with limited hardware, this is a real constraint, not a minor footnote.

Particle filters see heavy use in robotics (robot localization is a classic application), GPS-denied navigation, and augmented reality systems.

Also read: How to Implement Machine Learning Steps: A Complete Guide

Optical Flow: Tracking Pixel-Level Motion

Optical flow is different from the methods above. It doesn't track objects explicitly. It tracks motion at the pixel level, estimating how each pixel (or region) has moved between two frames.

Method / Aspect	Description	Best For
Lucas-Kanade Method	Tracks small, smooth movements using local pixel motion. Fast and efficient.	Feature tracking, real-time applications
Farneback Method	Computes motion for every pixel. More detailed but computationally heavier.	Dense motion estimation, video analysis
Applications	Detects moving objects, estimates camera motion, and tracks feature points.	Autonomous driving, robotics, surveillance
Limitation	Struggles with large, fast movements. Often combined with other tracking methods.	High-speed object tracking

Must read: Complete Guide to Types of Probability Distributions: Examples Explained

Siamese Networks: Deep Learning for Visual Tracking

Siamese networks brought a shift in how visual tracking is done. Instead of using handcrafted features or motion models, they learn to compare two patches of an image directly.

The setup is simple in concept. You have a target patch (what you're tracking) and a search region (where you're looking). The Siamese network takes both as input and produces a similarity score across the search region. The location with the highest score is where the object is.

SiamFC and SiamRPN are two well-known architectures in this family. SiamRPN adds a region proposal network on top, which makes it better at handling scale changes and aspect ratio shifts.

What makes these networks appealing is that they're fast at inference time. The template is processed once, and you only need to run the search side on each new frame.

Where they're used today:

• UAV tracking
• Sports player tracking
• General-purpose single-object visual tracking benchmarks like VOT and OTB

Also read: What is Probability Density Function? A Complete Guide to Its Formula, Properties and Applications

How to Choose the Right Tracking Method

There's no universal answer here. The right method depends on your constraints.

No single tracking method works best for every application. Your choice depends on motion patterns, the number of objects, available computing power, and how much accuracy your application requires. 

Ask yourself these questions before committing to one approach:

• How many objects are you tracking simultaneously?
• Do you need real-time performance or is latency acceptable?
• Will objects be occluded frequently?
• Do you have appearance information (visual features) available?
• What hardware will this run on?

Answering these honestly will narrow your options fast.

Conclusion

Tracking methods in machine learning range from mathematically elegant filters to deep learning architectures that can re-identify objects after occlusion. None of them is perfect. Each involves trade-offs between accuracy, speed, and complexity.

If you're starting out, SORT or Kalman filtering is the right place to begin. They're transparent, well-documented, and easier to debug. As your requirements grow, DeepSORT and Siamese networks give you more tools. And for robotics or sensor fusion work, particle filters remain hard to beat.

Pick based on your actual constraints, not theoretical performance numbers from benchmarks.

Ready to start your journey? Book a free consultation with upGrad today to find the best path for your career.