Skilled Support Personnel (SSP) serve emergency response organizations during an emergency incident, and include laborers, operating engineers, carpenters, ironworkers, sanitation workers and utility workers. SSP called to an emergency incident rarely have recent detailed training on the chemical, biological, radiological, nuclear and/or explosives (CBRNE) agents or the personal protection equipment (PPE) relevant to the incident. This increases personal risk to the SSP and mission risk at the incident site. Training for SSP has been identified as a critical need by the National Institute for Environmental Health Sciences, Worker Education and Training Program. We present a system being developed to address this SSP training shortfall by exploiting a new training paradigm called just-in-time training (JITT) made possible by advances in distance learning and cellular telephony. In addition to the current conventional training at regularly scheduled instructional events, SSP called to an emergency incident will have secure access to short (<5 minutes) training modules specific to the incident and derived from the Occupational Safety and Health Administration (OSHA) Disaster Site Worker Course. To increase retention, each learning module incorporates audio, video, interactive simulations, graphics, animation, and assessment designed for the user interface of most current cell phones. Engineering challenges include compatibility with current cell phone technologies and wireless service providers, integration with the incident management system, and SCORM compliance.
The diversity of first responders and of asymmetric threats precludes the effectiveness of any single training syllabus. Just-in-time training (JITT) addresses this variability, but requires training content to be quickly tailored to the subject (the threat), the learner (the responder), and the infrastructure (the C2 chain from DHS to the responder’s equipment). We present a distributed system for personalized just-in-time training of first responders. The authoring and delivery of interactive rich media and simulations, and the integration of JITT with C2 centers, are demonstrated. Live and archived video, imagery, 2-D and 3-D models, and simulations are autonomously (1) aggregated from object-oriented databases into SCORM-compliant objects, (2) tailored to the individual learner’s training history, preferences, connectivity and computing platform (from workstations to wireless PDAs), (3) conveyed as secure and reliable MPEG-4 compliant streams with data rights management, and (4) rendered as interactive high-definition rich media that promotes knowledge retention and the refinement of learner skills without the need of special hardware. We review the object-oriented implications of SCORM and the higher level profiles of the MPEG-4 standard, and show how JITT can be integrated into - and improve the ROI of - existing training infrastructures, including COTS content authoring tools, LMS/CMS, man-in-the-loop simulators, and legacy content. Lastly, we compare the audiovisual quality of different streaming platforms under varying connectivity conditions.
This paper presents a target acquisition and tracking system based on the biomimetic concept of foveal vision. The system electronically reconfigures the resolution, sizes, shape, and focal plane position of visual acuity to meet time- varying operational requirements while maximizing the relevance of acquired video. A reconfigurable multiresolution active pixel CMOS imaging array is integrated in a closed-loop fashion with video processing and configuration control. Imager and algorithm configuration is updated frame-by-frame and reactively to target and scene conditions. By dynamically tailoring the visual acuity of the senor itself, the relevance and acquired visual information is maximized and a fast update rate is achieved with reduced communications bandwidth and processing requirements throughout the entire system. The system also features small size and less power consumption, and does not require a pointing mechanism. The distinguishing features of reconfigurable foveal machine vision are presented, and the hardware and software architecture of the target acquisition and tracking system is discussed. Real-time experimental result for automated target search, detection, interrogation, and tracking are then presented.
The premise of foveal vision is that surveying a large area with low resolution to detect regions of interest, followed by their verification with localized high resolution, is a more efficient use of computational and communications throughput than resolving the area uniformly at high resolution. This paper presents target/clutter discrimination techniques that support the foveal multistage detection and verification of infrared-sensed ground targets in cluttered environments. The first technique uses a back-propagation neural network to classify narrow field-of-view high acuity image chips using their projection onto a set of principal components as input features. The second technique applies linear discriminant analysis on the same input features. Both techniques include refinements that address generalization and detected region of interest position errors. Experimental results using second generation forward looking infrared imagery are presented.
Foveal active vision features imaging sensors and processing with graded acuity, coupled with context-sensitive gaze control. The wide field of view of peripheral vision reduces target search time, but its low acuity makes it susceptible to preliminary false alarms when operating in environments with structured clutter. In this paper, we present a foveal active vision technique for multiresolution cueing that detects regions of interest (ROIs) with coarse resolution and subsequently interrogates with progressively higher resolution and ROIs are disambiguated. A hierarchical foveal machine vision framework with rectilinear retinotopology is used. A two-stage detector uses multiscale shape matching to identify potential targets and a chain of neural networks to filter out false alarms. This context-sensitive, coarse-to- fine approach minimizes the number of computationally expensive high acuity interrogates required, while preserving performance. Results from our experiments using second generation forward looking infrared imagery are presented.
This paper presents a target detection and interrogation techniques for a foveal automatic target recognition (ATR) system based on the hierarchical scale-space processing of imagery from a rectilinear tessellated multiacuity retinotopology. Conventional machine vision captures imagery and applies early vision techniques with uniform resolution throughout the field-of-view (FOV). In contrast, foveal active vision features graded acuity imagers and processing coupled with context sensitive gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision can operate more efficiently in dynamic scenarios with localized relevance than uniform acuity vision because resolution is treated as a dynamically allocable resource. Foveal ATR exploits the difference between detection and recognition resolution requirements and sacrifices peripheral acuity to achieve a wider FOV (e.g. faster search), greater localized resolution where needed (e.g., more confident recognition at the fovea), and faster frame rates (e.g., more reliable tracking and navigation) without increasing processing requirements. The rectilinearity of the retinotopology supports a data structure that is a subset of the image pyramid. This structure lends itself to multiresolution and conventional 2-D algorithms, and features a shift invariance of perceived target shape that tolerates sensor pointing errors and supports multiresolution model-based techniques. The detection technique described in this paper searches for regions-of- interest (ROIs) using the foveal sensor's wide FOV peripheral vision. ROIs are initially detected using anisotropic diffusion filtering and expansion template matching to a multiscale Zernike polynomial-based target model. Each ROI is then interrogated to filter out false target ROIs by sequentially pointing a higher acuity region of the sensor at each ROI centroid and conducting a fractal dimension test that distinguishes targets from structured clutter.
This paper describes an algorithm for hierarchical shape classification based on multiresolution skeletons, and its application to the detection and identification of objects in noisy, cluttered imagery. A pyramid of stored two dimensional templates is employed to identify the object class, its location and spatial orientation. The skeleton of the object is selected for shape representation in this paper since it is a good 2D shape descriptor and relatively robust. The morphologically computed skeleton is an implementation of the medial axis transform. A real- time recognition scheme based on Borgefors' chamfer matching technique is presented which employs multiresolution top-down matching of object medial axis skeletons in a 4:1 pyramid. The proliferation of candidate points at higher resolution is controlled with a clustering scheme. In order to permit small and simply shaped objects to be discriminated from large, complex objects whose skeletons are supersets, we introduce negative match weight scores on the subset of the polygon discriminating the two templates. Results with training sets of noisy and cluttered images are presented. This scheme is shown to be capable of real-time detection and characterization of targets with good reliability in a test scenario.