Object Inter-Camera Tracking with nonoverlapping views: A new dynamic
approach
Trevor Montcalm
Bubaker Boufama
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
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?
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
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
Background Subtraction
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
Blob Formation
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
Inter-Camera Tracking
 The specific task of
object tracking across
camera views that are
non-overlapping
 Each camera has a
separate field of
vision
Features Used for Object Tracking
Basic Features:
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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)
Features Used for Object Tracking
Advanced Features:
 Histogram – Color histogram of the object
 Shape – 49 Zernike Moments
 All feature values are normalized to
facilitate comparison between different
cameras
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
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
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
Camera Link Quality Example
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
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
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
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
Experimental Results
 Two cameras used: Sony Cyber-shot DSCS930 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
Experimental Results
Experimental Results
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.