Low resolution vehicle re-identification based on appearance features for wide area motion imagery

Abstract

The description of vehicle appearance in Wide Area Motion Imagery (WAMI) data is challenging due to low resolution and renunciation of color. However, appearance information can effectively support multiple object tracking or queries in a real-time vehicle database. In this paper, we present a systematic evaluation of existing appearance descriptors that are applicable to low resolution vehicle reidentification in WAMI data. The problem is formulated as a one-to-many re-identification problem in a closed-set, where a query vehicle has to be found in a list of candidates that is ranked w.r.t. their matching similarity. For our evaluation we use a subset of the WPAFB 2009 dataset. Most promising results are achieved by a combined descriptor of Local Binary Patterns (LBP) and Local Variance Measure (VAR) applied to local grid cells of the image. Our results can be used to improve appearance based multiple object tracking algorithms and real-time vehicle database search algorithms.