1. Real-time object tracking using Kalman filter Siddharth Verma P.hD. Candidate Mechanical Engineering

2. Object tracking To continuously find an object of interest in the scene.

3. Object tracking can be approached using many ways Electro-Magnectic sensors GPS systems Time of flight method using sonar or LASER Vision based tracking.

4. 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

5. 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

6. Some applications Security and surveillance Recognize people Detect intruders Car parking Traffic management Medical Imaging Sports and Biomechanics Geological exploration Astronomy exploration

7. 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 algorithm Intruder alert system for autonomous surveillance.

8. Object tracking Modeling of the scene Steady state tracking

9. Modeling of the scene Pixel map of intensity and location of the background Averaged over many images to remove any random noise

10. 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.

11. 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.

13. Limitations Backgrounds color becomes same as the object color. Lightening changes High frequency image acquisition Highly random object motion