Advances in large scale visual sensors enable (near) real time acquisition of imagery data over wide urban surveillance areas. Data collected by such sensors, such as the wide area motion imagery (WAMI) systems, however poses computational challenges to existing visual analysis algorithms. Among many tasks, tracking multiple moving target indicators (MTI) is of fundamental importance since it bridges the low-level imagery input (e.g., WAMI data) and the high-level situation awareness (e.g., trajectory prediction and understanding). In this project, we plan to design and implement a novel high-performance-computing (PHC) enabled multiple-target-tracking (MTT) framework, named HPC-MTT, with focus on large scale MTI tracking in the context of WAMI understanding. The design of HPC-MTT will be from three inter-correlated viewpoints. First, from the MTT point of view, we plan to parallelize all steps in the MTT pipeline, including background subtraction, MTI detection and multiple-target-association (MTA). Second, from the HPC point of view, we plan to design WAMI-specified parallelization algorithms following the partition-communication-agglomeration-mapping (PCAM) paradigm. In particular, we will design spatial PCAM, temporal PCAM and algorithmic PCAM to capture respectively the spatial, temporal and algorithmic characteristics of WAMI tasks. Finally, from the HPC platform point of view, we will deploy the proposed HPC-MTT in various HPC infrastructures including multiple-core computation (e.g., GPGPU for on-board tasks) and cloud computing (e.g., Amazon E2C-based prototype for off-board tasks).