Real-time object tracking using Kalman filter

Siddharth VermaSiddharth Verma

P.hD. Candidate

Mechanical Engineering

Object tracking

 

To continuously find an object of interest in the scene.

Object tracking can be approached using many ways

• Electro-Magnectic sensors

• GPS systems

• Time of flight method using sonar or LASER

• Vision based tracking. 

Advantages of Vision-based techniques

• Vision data is easy to visualize and understand

• Provide additional data for further processing

• Object recognition

• Gesture recognition

• Sign language recognition

Scope of object tracking

• Real-time / Offline processing

• 2D / 3D Tracking

• Tracking position / Velocity/ Acceleration

• Single object / Multiple object tracking

• Single camera / Multiple cameras-based tracking

• Fixed camera / Moving cameras

• Fixed ambient conditions / Outdoor tracking

• Precision requirement for the object being tracked

Some applications

  

• Security and surveillance

• Recognize people

• Detect intruders

• Car parking

• Traffic management

• Medical Imaging

• Sports and Biomechanics

• Geological exploration

• Astronomy exploration

Problem Statement

• Real-time object tracking• 2D object tracking using single camera view• Single object tracking• Fixed camera position• Fixed ambient conditions• Office background• Handle occlusion – using Kalman filters Possible application of the algorithmIntruder alert system for autonomous surveillance.

Object tracking

• Modeling of the scene

• Steady state tracking

Modeling of the scene

• Pixel map of intensity and location of the background

• Averaged over many images to remove any random noise

Steady state tracking

• Look for any change of background by Image subtraction

• Find blobs in the image that are a group of connected pixels.

• Remove all the blobs that have area less than the largest blob.

• Check consistency of area of interest

• Develop instantaneous texture and location map of the object.

• Center of gravity(CG) of the object is tracked in each frame.

• The measured CG position in the current image is updated using the Kalman filter equations to account for occlusion.

• CG position in the incoming frame is predicted using kalman filtering technique using simple Newton dynamic equations

• The predicted value of CG is used to make a bounding box around the object, to localize processing in the incoming image.

 Results

• Successfully track the position of the 2D position of center of gravity of a object moving against a stationary background.

• The technique can handle partial occlusion.

• The tracking was performed offline but same program could be used for real-time tracking.

Limitations • Backgrounds color becomes same as the object color.

• Lightening changes

• High frequency image acquisition

• Highly random object motion