Visual Tracking and Identification of Targets in Limited Spatial Resolution

Project: Research project

Project Details


Visual Tracking and Identification of Targets in Limited Spatial Resolution
Extending visual sensing capacity in target tracking and identification is very important for
Army’s future force. Contemporary techniques assume and depend on good quality images and
video, as visual details need to be used for target modeling. In practice, however, as the environments
are unconstrained, their performances are largely degraded when the quality of images and
video is seriously degraded. The existing solution to this issue is to perform pre-processing to
enhance and restore the quality of the imagery, e.g., via image super-resolution or deblurring, before
performing target detection, tracking and recognition. However, such image restoration tasks
themselves are very difficult, and they generally are very computationally demanding. Therefore,
this solution is not practical.
This project is targeted on exploring a general and unified approach that does not perform explicit
image restoration as pre-processing in performing target tracking and recognition under lowquality
imagery, such as poor image resolution, sever blurs, and other image/video degradation.
Although this project is focused on one case study of limited spatial resolution, the proposed new
approach is expected to be generally applicable to other situations of visual degradation. Specifically,
we plan to address the following issues:
� A principled approach and its theoratical foundation. The key idea of avoiding performing
explicit image restoration is to embed the prior knowledge for restoration into target
matching. Our objective is to develop a general approach of learning image similarity and
regression that applies to various situations of low-quality imagery.
� Target matching and tracking. The performance of target tracking is largely determined by
the metric for image similarity. It should be adaptive to different imagery. Our objective is
to learn the metric for limit-resolution imagery by steering the known similarity metric in
good-resolution images, based on an exemplar database conveying the knowledge.
� Target identification. Targets are seen from very different views. It is very difficult to determine
if the low-quality images from different views are the same target. Our objective is
to learn the visual similarity metrics to generate the predicted novel views. The difference
between the actual image and predicted view will be used for better target identification.
One of its innovations is that it avoids performing explicit and dedicated image/video restoration
which is in general computationally demanding, but rather using the low-quality data directly
with the implicit knowledge learned from data. This research has not been studied before, and it
leads to very efficient and innovative solutions with very wide applications.
Moreover, it is a principled and general solution that will be able to handle various image
degradations in the same framework. Once we have collected the training data (i.e., an exemplar
database) that associate the low-quality data with their corresponding high-quality data, the implicit
knowledge conveyed in this exemplar database facilitate to learn the similarity metric of the lowquality
data, and it will empower effective reconstruction-based local regression. This will make
possible target matching, tracking and recognition on low-quality data.
In addition, image restoration and enhancement (e.g., super resolution) can also be done in the
proposed new approach via reconstruction-based local regression
Effective start/end date9/1/155/31/16


  • Army Research Office (W911NF-15-1-0472)


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