Challenge Statement

The Department of National Defence (DND) is looking for novel concepts, approaches, techniques and technologies to enable and augment the ability of the Canadian Armed Forces (CAF) and security personnel to:

• detect, recognize, and identify persons or objects of interest in a physical environment, and/or
• track identified persons and objects of interest using seamless information sharing across a decision network.

Of particular interest are methods supporting the soldier or emergency responder to:

• minimize or manage cognitive load;
• support real-time identification and tracking of objects for timely decision making;
• leverage and fuse multiple sensor and data sources;
• enable operations in complex environments including urban settings with the presence of cooperative and non-cooperative targets (i.e. disguised, camouflaged, or concealed); and differentiate between combatants and non-combatants.

Background and Context

CAF and security personnel must operate in complex and dynamic environments, and in the presence of physical threats while maintaining decision advantage. This challenge and capability solution must be geared toward the preservation of soldier safety in operations, the achievement of threat identification and possible avoidance, the minimization of collateral damages, and the achievement of improved speed, accuracy, confidence, assurance, and precision of impact as part of the operations decision-action cycle. It is also intended to improve individual and common situational awareness and understanding for mission success in both domestic and expeditionary operations.

Outcomes and Considerations

Desired outcomes of this effort will improve situational awareness and understanding, and support decision making by:

• Identifying and tracking objects/person of interest in real time;
• Successful cross-cueing of information from multiple sensors;
• The ability to coordinate and hand-off to partners who are conducting follow-on actions;
• Efficient use of resources, and low error rates (i.e. false positives, false negatives); and
• Seamless interoperability among operators and link to higher command levels.

This challenge applies to day and night situations, as well as in the presence of adverse weather conditions and noise.

Proposals should address privacy and ethical concerns.

For this challenge, DND is not interested in the broader challenges of social media analytics or full motion video (FMV) analysis and associated automation algorithms. These matters are covered by separate challenges. However, exploitation of the output of such related methods and sources is relevant. DND is interested not only in novel capabilities and concepts, but also in practical solutions which could be fielded and implemented quickly in the next few years.