Near real-time, on-the-move software PED using VPEF

Abstract

The scope of the Micro-Cloud for Operational, Vehicle-Based EO-IR Reconnaissance System (MOVERS) development effort, managed by the Night Vision and Electronic Sensors Directorate (NVESD), is to develop, integrate, and demonstrate new sensor technologies and algorithms that improve improvised device/mine detection using efficient and effective exploitation and fusion of sensor data and target cues from existing and future Route Clearance Package (RCP) sensor systems. Unfortunately, the majority of forward looking Full Motion Video (FMV) and computer vision processing, exploitation, and dissemination (PED) algorithms are often developed using proprietary, incompatible software. This makes the insertion of new algorithms difficult due to the lack of standardized processing chains. In order to overcome these limitations, EOIR developed the Government off-theshelf (GOTS) Video Processing and Exploitation Framework (VPEF) to be able to provide standardized interfaces (e.g., input/output video formats, sensor metadata, and detected objects) for exploitation software and to rapidly integrate and test computer vision algorithms. EOIR developed a vehicle-based computing framework within the MOVERS and integrated it with VPEF. VPEF was further enhanced for automated processing, detection, and publishing of detections in near real-time, thus improving the efficiency and effectiveness of RCP sensor systems.

Real-time buried threat detection and cueing capability in VPEF environment

“…In dynamic contrast mode, contrast setting for morphological processing is adapted from frame to frame such that, on an average, the baseline module is expected to produce a specified number of detections. After processing, the baseline module generates a list of pixel coordinates of potential targets in the form of VPEF Detected Objects [11]. Apart from the pixel location and corresponding geolocation, each detected object carries the timestamp of the image frame and a unique ID within that frame.…”

“…The design of each plugin has a set of well-defined input and output variables. When connected to a coherent processing pipeline, these plugins can be executed in VPEF multi-thread environment [11] for realtime buried threat detection. Moreover, following GStreamer standard, these plugins can interact with different plugins developed by third parties.…”