KRMARO: Aerial Detection of Small-Size Ground Moving Objects Using Kinematic Regularization and Matrix Rank Optimization

Abstract

Detecting moving objects has been well studied in the past due to its importance in computer vision applications. Nevertheless, in aerial imagery, the small sizes of moving objects and the camera motion present challenges to existing well-known detection methods. Most moving object detection methods have reported either high true detection rates associated with high false-detection rates, or low false-detection rates at the expense of lowering true detection rates. This paper proposes a novel method, Kinematic Regularization and Matrix Rank Optimization (KRMARO), to achieve high true-detection rates and reduce false-detection rates significantly. KRMARO introduces a formulation of the moving objects detection problem that integrates a novel kinematic regularization into the principal component pursuit. This formulation models moving objects as sparse, which is located in regions exhibiting unique kinematic properties, while the background is modeled as a low-rank matrix that is corrupted by this sparse. To solve the former formulation accurately, KRMARO proposes a solution based on the inexact Newton method and the inexact augmented Lagrange multiplier with backtracking behavior. The robustness of KRMARO is verified through testing on DARPA VIVID, UCF aerial action, and VIRAT aerial data sets and then comparing the results with relevant state-of-the-art methods.