Hierarchical Closely-Spaced Object (CSO) Resolution for IR Sensor Surveillance Daniel Macumber, Sabino Gadaleta, Allison Floyd, and Aubrey Poore Numerica Corporation, P.O. Box 271246, Fort Collins, CO 80527 ABSTRACT The observation of closely-spaced objects using limited-resolution Infrared (IR) sensor systems can result in merged object measurements on the focal plane. These Unresolved Closely-Spaced Objects (UCSOs) can significantly hamper the performance of surveillance systems. Algorithms are desired which robustly resolve UCSO signals such that (1) the number of targets, (2) the target locations on the focal plane, (3) the uncertainty in the location estimates, and (4) the target intensity signals are correctly preserved in the resolution process. Furthermore, decomposition of UCSO objects must be done in a way which will not overwhelm a tracking system in the event of a sudden increase in the number of objects. This paper presents a framework for obtaining UCSO resolution while meeting tracker real-time computing requirements by applying processing algorithms in a hierarchical fashion. Image restoration techniques, which are often quite cheap, will be applied first to help reduce noise and improve resolution of UCSO objects on the focal plane. The CLEAN algorithm, developed to restore images of point targets, is used for illustration. Then, when processor constraints allow, more intensive algorithms are applied to further resolve USCO objects. A novel pixel-cluster decomposition algorithm that uses a particle distribution representative of the pixel-cluster intensities to feed the Expectation Maximization (EM) is used in this work. We will present simulation studies that illustrate the capability of this framework to improve correct object count on the focal plane while meeting the four goals listed above. In the presence of processing time constraints, the hierarchical framework provides an interruptible mechanism which can satisfy real-time run-time constraints while improving tracking performance. Keywords: Infrared Sensor Surveillance, Pixel (Clump) Cluster Tracking, Single and Multi-Assignment, Pixel-Cluster Decomposition, Multiple Hypothesis Pixel-Cluster Decomposition 1. INTRODUCTION When viewing point targets with IR sensors, pixels on the sensor focal plane above a certain threshold are taken to repre- sent objects of interest and are input to a tracking system for position estimation and target recognition. Measured signals from Closely Spaced Objects (CSOs) may overlap on the sensor focal plane, forming a connected pixel-cluster which represents multiple objects. These Unresolved Closely Spaced Objects (UCSOs) both reduce the effective resolution of the measurement and obscure the number of true targets in the scene. Multiple Hypothesis Pixel-Cluster Decomposition (MHPCD) tracking methods have been developed 1 to track UCSO objects but tend to grow computationally expensive as the number of pixel-cluster decomposition hypotheses increases. In addition these methods do not allow for individual objects within a USCO to be identified early, which is critical for target recognition. An approach which can accurately decompose UCSOs early in flight is desired. However, it is not always advantageous to immediately decompose all objects in the scene as a sudden, large increase in the number of reported objects may overwhelm the track initiation problem. Therefore, this paper proposes an interruptible, hierarchical approach to pixel-cluster decomposition. In this approach relatively cheap image restoration algorithms are used first to increase sensor resolution as CSO objects begin to form in the scene. Then, after the initial computational needs of track initiation decrease, more complicated CSO algorithms are applied which further decompose UCSO objects. These algorithms may be applied to individual pixel-clusters based on needs and computational ability of the tracker. Furthermore, data from other sensors and the global track database may be used to aid the USCO decomposition by providing prior decomposition Further author information: (Send correspondence to A.F or A.P.) A.F.: E-mail: lafloyd@numerica.us, Telephone: (970) 419 8343 A.P.: E-mail: abpoore@numerica.us, Telephone: (970) 419 8343