EVA

The HMGCC Edge-Vision Analytics (EVA) Challenge invites organisations to develop edge-AI solutions that dramatically reduce power consumption in visual surveillance systems. Traditional surveillance relies on high-quality cameras running continuously, which drain batteries and generate large volumes of data. HMGCC’s challenge aims to replace these with edge devices that process low-resolution sensor feeds locally and only activate high-power cameras when a target of interest is detected. The programme provides £60 k funding for a 12-week project, plus time, materials and overheads, to deliver a proof of concept. Applicants are expected to design a demonstrator operating within TRL 3–6 (technology readiness levels). The context emphasises that global surveillance often occurs in remote or hostile environments without mains power, so devices must be battery-operated, low-power and robust. The challenge highlights a use case: an investigator named Lucy needs to monitor two clandestine drug labs. She normally uses passive infrared (PIR) sensors to trigger a camera but faces false alarms and battery drain. By connecting a low-power camera to an edge-AI device programmed to recognise specific vehicles or individuals, she can filter out irrelevant movement. This system eliminates more than 99 % of the visual imagery, conserving power and reducing analyst workload. The challenge encourages innovation in multi-modal sensing (e.g., audio, radar, low-light cameras) to improve trigger reliability. Ultimately, HMGCC seeks to develop surveillance technology that functions off-grid, reduces radio-frequency signatures and improves operational efficiency.