A major challenge for ATR evaluation is developing an accurate image truth that can be compared to an ATR algorithm's decisions to assess performance. We have developed a semi-automated video truthing application, called START, that greatly improves the productivity of an operator truthing video sequences. The user, after previewing the video selects a set of salient frames (called "keyframes"), each corresponding to significant events in the video. These keyframes are then manually truthed. We provide a spectrum of truthing tools that generates truth for additional frames from the keyframes. These tools include: fully-automatic feature tracking, interpolation, and completely manual methods. The application uses a set of diagnostic measures to manage the user's attention, flagging portions in the video for which the computed truth needs review. This changes the role of the operator from raw data entry, to that of expert appraiser supervising the quality of the image truth. We have implemented a number of graphical displays summarizing the video truthing at various timescales. Additionally, we can view the track information, showing only the lifespan information of the entities involved. A combination of these displays allows the user to manage their resources more effectively. Two studies have been conducted that have shown the utility of START: one focusing on the accuracy of the automated truthing process, and the other focusing on usability issues of the application by a set of expert users.
A major challenge for ATR evaluation is developing an accurate image truth that can be compared to an ATR algo-rithm's decisions to assess performance. While many standard truthing methods and scoring metrics exist for stationary targets in still imagery, techniques for dealing with motion imagery and moving targets are not as prevalent. This is par-tially due to the fact that the moving imagery / moving targets scenario introduces the data association problem of as-signing targets to tracks. Video datasets typically contain far more imagery than static collections, increasing the size of the truthing task. Specifying the types and locations of the targets present for a large number of images is tedious, time consuming, and error prone. In this paper, we present an updated version of a complete truthing system we call the Scoring, Truthing, And Registration Toolkit (START). The application consists of two components: a truthing compo-nents that assists in the automated construction of image truth, and a scoring component that assesses the performance of a given algorithm relative to the specified truth. In motion imagery, both stationary and moving targets can be de-tected and tracked over portions of a motion imagery clip. We summarize the capabilities of START with emphasis on the target tracking and truthing diagnostics. The user manually truths certain key frames, truth for intermediate frames is then inferred and sets of diagnostics verify the quality of the truth. If ambiguous situations are encountered in the inter-mediate frames, diagnostics flag the problem so that the user can intervene manually. This approach can dramatically reduce the effort required for truthing video data, while maintaining high fidelity in the truth data. We present the results of two user evaluations of START, one addressing the accuracy and the other focusing on the human factors aspects of the design.