Feature variations for a static vehicle track from airborne image sequences

Abstract

Selecting the correct feature set is an essential basis for video sequence analysis that leads to applications such as tracking and recognition of vehicles. This paper selects diverse multiple features and tests their accuracy for tracking a static vehicle. The static vehicle images are captured with airborne infrared and color video cameras. The camera collects 30 frames per second of compressed video in Motion JPEG format. The diverse features are selected from representative histogram-based, invariant-based, spatial-temporal-based and the center-symmetric autocorrelation-based family of features. A small dataset of airborne video sequences that include static vehicles with variations in quality, orientation, resolution, foreground lighting and background lighting are used to test feature selection and static tracking. The track of the vehicles is hand selected frame-to-frame to create a truth track. The result of each feature to maintain track is tested and scored based on distance from the truth track. Once a significant break from track occurs, the truth data is used to reacquire track. One goodness score is based on how often a feature breaks track. This analysis shows promise for identifying appropriate features for improved tracking results. The suggested algorithm is demonstrated on only a few video sequences with limited variations in operating conditions but demonstrates improvement possibilities for near real-time application.