Video Warehousing and Face Classification Visualization
Applications such as surveillance or customer behavior analysis will require visualization and analysis of historical meta-data produced by various image processing algorithms (such as the face detection and classification algorithms). In order to allow for efficient data mining and visualization of such data, we proposed introducing a standard database layer responsible for managing the "video data-warehouse". As a first application of this approach, we created a framework to visually compare the results a face detection algorithm with ground truth data, as well as comparing the results of different algorithms.
Background & Objective: A modular approach to software creation assures that applications are more flexible, robust, and are easier to maintain. By creating a database layer for applications that rely on metadata produced by video analysis (such as the face detection or face recognition algorithms), we make these modular. We often create large datasets for use in testing and improving the basic algorithms. At the same time these datasets can be used to test innovative approaches to visualization of large video and image data. Here we demonstrate this approach with an application that helps to verify and improve the effectiveness of the face detection algorithm. By allowing the user to compare the results of different versions of the algorithm, we create a regression testing framework. Comparing the algorithm with hand labeled ground truth helps determine which images are particularly challenging. This will make improving training set selection more efficient.
Technical Discussion: The database component of the application was constructed to interact with CHO NET objects that broadcast data over networks using a (MERL patent-pending) real time protocol. This allowed the detection component to be decoupled from other applications. Stored data can be rebroadcast imitating the original CHO NET stream, or can be accessed directly. Direct access to the underlying database (MySQL) can be made from Java via JDBC, or from C/C++ via ODBC/TCL.
Technology Area: Computer Vision
Modification Date: January 23, 2007