1. Object Inter-Camera Tracking with non- overlapping views: A new dynamic approach Trevor Montcalm Bubaker Boufama

2. Layout of Todays Presentation  Basics of Object Tracking, bottom to top overview  Single Camera and Inter-Camera Tracking  Features used for Object Tracking  Camera Linking  Emphasizing factors  Experimental Results

3. What is Object Tracking?  The task of tracking objects as they move within an area under video surveillance o Objects could be people, cars, anything of interest  How is this accomplished?

4. Other significant works  Mohammed Ahsan Ali o Feature-based tracking  Andrew Gilbert o Matrix-based color transfer functions between cameras  Y. Cai, J. Kang o Advanved shape and color descriptor used to match objects

5. Background Subtraction  Subtracts a background model from the current frame to classify which pixels are foreground and background  Foreground pixels are of interest, objects in the scene  The Adaptive Gaussian Mixture Model background subtraction algorithm was used

6. Background Subtraction

7. Blob Formation  A blob is a group of foreground pixels that might be a real-life object  Decides which groups are blobs, and which are noise  Three steps: o Smooth background subtracted image o Use Connected Component Analysis to discover groups o Blob size thresholding, merging if close enough

8. Blob Formation

9. Single camera object tracking  Matches blobs to the set of known objects in the scene o Done for each frame of video  Matching is accomplished by comparing the feature vector of each blob and object o A feature vector is a collection of features o Each feature describes a property of the object or blob  Occlusions handled with Kalman filter

10. Inter-Camera Tracking  The specific task of object tracking across camera views that are non-overlapping  Each camera has a separate field of vision

11. Features Used for Object Tracking  Location – The current centroid of an object  Velocity – Objects 2D velocity (pixels/sec)  Width – Object width  Height – Object height  Size – Object size (# of foreground pixels) Basic Features:

12. Features Used for Object Tracking  Histogram – Color histogram of the object  Shape – 49 Zernike Moments  All feature values are normalized to facilitate comparison between different cameras Advanced Features:

13. Comparing Feature Vectors  Single camera object tracking: o Differences of all features are averaged for a final difference  Inter-camera object tracking: o Individual features are emphasized or depreciated, depending on circumstances o This is the new dynamic approach mentioned in the title

14. Emphasizing Factors  Time: Emphasize more recent appearances  Camera Link Quality: Use previous matching information to systems advantage  Stability: Emphasize more stable features over unstable ones

15. Camera Link Quality  Between each pair of cameras is a camera link  Stores a Camera Transfer Function, which translates a feature vector from one camera to another  Idea is to use previous matching history to translate features o Exploit redundancy in object movement patterns

16. Camera Link Quality Example

17. Building the Matching Feature Vector  An aggregate feature vector used to represent the object in matching o Aggregation of many appearances  Time: More recent appearances are used  Camera Link Quality: Reliably translated features are emphasized  Stability: More stable features are emphasized

18. Building the Matching Feature Vector  Each feature vector translated to a target camera  Using recentness, translation quality, and feature quality, a single matching feature vector is built

19. Dynamic Weighting  Describes how to weigh each feature in a feature vector comparison, similar to matching feature vector  Emphasizes robust features for low-camera link quality  After matching data built up, more general features are weighed in

20. Object tracking decision  Best object/blob match is chosen, compared against a threshold  Single camera tracking: Preset threshold  Inter-camera tracking: Dynamic threshold. o At first, a low threshold (0.65) o After matching data is built up, more stringent threshold (0.95) o Change in threshold is linear

21. Experimental Results  Two cameras used: Sony Cyber-shot DSC- S930 and a Kodak EasyShare C180 o Low-resolution, off the self cameras with differing color sensitivity  Surveillance videos filmed in two locations: o A large building hallway o Domestic house

22. Experimental Results

24. References  A. Gilbert and R. Bowden. Incremental, scalable tracking of objects inter camera. Computer Vision and Image Understanding, 111(1):43 – 58, 2008. Special Issue on Intelligent Visual Surveillance (IEEE).  M. Ali. Feature-based tracking of multiple people for intelligent video surveillance. In Masters Abstracts International, volume 45, 2006.  J. Kang, I. Cohen, and G. Medioni. Persistent objects tracking across multiple non overlapping cameras. In Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION’05)- Volume, volume 2, pages 112–119.  Y. Cai, K. Huang, and T. Tan. Human Appearance Matching Across Multiple Non-overlapping Cameras. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pages 1–4, 2008.