The Satellite Survivability Module (SSM) is an end-to-end, physics-based, performance prediction model for the effects of adversarial directed energy engagement of orbiting spacecraft. SSM was created as an add-on module for the Satellite Tool Kit (STK). Two engagement types are currently supported: laser engagement on the focal plane array of an imaging spacecraft and Radio Frequency (RF) engagement of spacecraft components. This paper will focus on the laser engagement scenario, the process by which it is defined, and how we use actual laser effects data to help validate the SSM tool. SSM allows the user to create and implement a variety of “what if” scenarios. Satellites can be placed in a variety of orbits. Threats can be placed anywhere on the Earth or, for version 2.0, on other satellites. Satellites and threats can be mixed and matched to examine possibilities. Protection techniques for a particular spacecraft can be turned on or off individually, and can be arranged in any order to simulate more complicated protection schemes. Results can be displayed as 2-D or 3-D visualizations, or as textual reports. A new report feature available in version 2.0 will allow laser effects data to be displayed dynamically during scenario execution. In order to test SSM capabilities, the Ball team used SSM to model several engagement scenarios using actual lab and field test data. Actual laser, optics, and detector characteristics were entered into SSM to determine what effects we can expect to see, and to what extent. We concluded that SSM results match reasonably close to actual lab and field test results.

The goal of this work is to detect and track the articulated pose of a human in signing videos of more than one hour in length. In particular we wish to accurately localise hands and arms, despite fast motion and a cluttered and changing background.We cast the problem as inference in a generative model of the image, and propose a complete model which accounts for self-occlusion of the arms. Under this model, limb detection is expensive due to the very large number of possible configurations each part can assume. We make the following contributions to reduce this cost: (i) efficient sampling from a pictorial structure proposal distribution to obtain reasonable configurations; (ii) identifying a large number of frames where configurations can be correctly inferred, and exploiting temporal tracking elsewhere.Results are reported for signing footage with challenging image conditions and for different signers. We show that the method is able to identify the true arm and hand locations with high reliability. The results exceed the state-of-the-art for the length and stability of continuous limb tracking.

We present several contributions towards automatic recognition of BSL signs from continuous signing video sequences: (i) automatic detection and tracking of the hands using a generative model of the image; (ii) automatic learning of signs from TV broadcasts of single signers, using only the supervisory information available from subtitles; (iii) discriminative signer-independent sign recognition using automatically extracted training data from a single signer. Our source material consists of many hours of video with continuous signing and aligned subtitles recorded from BBC digital television. This is very challenging material visually in detecting and tracking the signer for a number of reasons, including self-occlusions, self-shadowing, motion blur, and in particular the changing background; it is also a challenging learning situation since the supervision provided by the subtitles is both weak and noisy.

The goal of this work is to automatically learn a large number of British Sign Language (BSL) signs from TV broadcasts. We achieve this by using the supervisory information available from subtitles broadcast simultaneously with the signing.This supervision is both weak and noisy: it is weak due to the correspondence problem since temporal distance between sign and subtitle is unknown and signing does not follow the text order; it is noisy because subtitles can be signed in different ways, and because the occurrence of a subtitle word does not imply the presence of the corresponding sign.The contributions are: (i) we propose a distance function to match signing sequences which includes the trajectory of both hands, the hand shape and orientation, and properly models the case of hands touching; (ii) we show that by optimizing a scoring function based on multiple instance learning, we are able to extract the sign of interest from hours of signing footage, despite the very weak and noisy supervision.The method is automatic given the English target word of the sign to be learnt. Results are presented for 210 words including nouns, verbs and adjectives.

The goal of this work is to detect hand and arm positions over continuous sign language video sequences of more than one hour in length. We cast the problem as inference in a generative model of the image. Under this model, limb detection is expensive due to the very large number of possible configurations each part can assume. We make the following contributions to reduce this cost: (i) using efficient sampling from a pictorial structure proposal distribution to obtain reasonable configurations; (ii) identifying a large set of frames where correct configurations can be inferred, and using temporal tracking elsewhere. Results are reported for signing footage with changing background, challenging image conditions, and different signers; and we show that the method is able to identify the true arm and hand locations. The results exceed the state-of-the-art for the length and stability of continuous limb tracking.