Object Inter-Camera Tracking with non- overlapping 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 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:
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:
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 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
Experimental Results
References A. Gilbert and R. Bowden. Incremental, scalable tracking of objects inter camera. Computer Vision and Image Understanding, 111(1):43 – 58, 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, 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, ICPR th International Conference on, pages 1–4, 2008.