Object tracking is a fundamental technique in computer vision and robotics. It involves detecting and tracking the movement of objects in a video stream. This page provides an overview of object tracking, its applications, and the techniques used.
Techniques Used in Object Tracking
1. Optical Flow
Optical flow is a method that measures the movement of pixels between two consecutive frames. It's often used for simple tracking tasks.
2. Kalman Filter
The Kalman filter is a recursive data processing algorithm used for estimating the state of a linear dynamic system from noisy measurements.
3. Mean-Shift
Mean-shift is a non-parametric clustering technique used for object tracking. It works by shifting the window to the mean of the pixel intensities in the window.
4. Deep Learning
Deep learning models, such as Convolutional Neural Networks (CNNs), have been widely used for object tracking due to their ability to learn complex patterns.
Applications of Object Tracking
- Autonomous Vehicles: Object tracking is crucial for enabling autonomous vehicles to detect and navigate around obstacles.
- Surveillance: Object tracking can be used for monitoring public spaces and detecting suspicious activities.
- Robotics: Object tracking is essential for robots to interact with their environment.
For more information on object tracking and its applications, check out our Introduction to Robotics.