The goal of the DARPA Video Verification of Identity (VIVID) program is to develop an automated video-based ground targeting system for unmanned aerial vehicles. The system comprises several modules that interact with each other to support tracking of multiple targets, confirmatory identification, and collateral damage avoidance. The Multiple Target Tracking (MTT) module automatically adjusts the camera pan, tilt, and zoom to support kinematic tracking, multi-target track association, and confirmatory identification. The MTT system comprises: (i) a video processor that performs moving object detection and feature extraction, including object position and velocity, (ii) a multiple hypothesis tracker that processes video processor reports to generate and maintain tracks, and (iii) a sensor resource manager that aims the sensor to improve tracking of multiple targets. This paper presents a performance assessment of the current implementation of the MTT under several operating conditions. The evaluation is done using pre-recorded airborne video to assess the ability of the video tracker to detect and track ground moving objects over extended periods of time. The tests comprise a number of different operational conditions such as multiple targets and confusers under various levels of occlusion and target maneuverability, as well as different background conditions. The paper also describes the challenges that still need to be overcome to extend track life over long periods of time.