SURVANT developed a product that introduces solutions beyond the industry state-of-the-art to face the challenges identified in the markets targeted. A comprehensive list of those challenges, the solutions envisioned and the ambition involved is provided:
1 – Video analysis employing Artificial Intelligence and Deep Learning techniques; 2 – Event Enrichment and Semantic Reasoning; 3 – Video and Image indexing; 4 – Intelligent, human-in-the-loop, GIS-based user interface; 5 – Scalable System Architecture to address the explosion of available video content; 6 – Easily Deployable using a Docker-based system deployment; 7 – Legal and ethical issues and LEP framework.
Based on the results already achieved, key functionalities that can provide high added value for potential customers compared to the competition have been identified and presented below: 1 – Situational awareness framework; 2 – Advanced content-based search; 3 – Search expansion tools.
Addressing target audiences and expressing needs
- Business Angels
- Other type of Investment
The system can be deployed on large operational environments thanks to investments of Business Angels or any other kind of private investment. Even potential contacts coming from stipulated contracts of the following list could be funds that could be used to experiment different real operational contexts:
- LEAs
- Municipalities and urban surveillance projects
- Mass transit monitoring and security critical infrastructure protection
- Military and intelligence applications
- Banks
- Small critical infrastructures
- Large facilities or installations
- Public or private funding institutions
- Private Investors
R&D, Technology and Innovation aspects
The SURVANT system has been developed and validated during the lifetime of the project. Both functionalities and usability aspects have been vigorously evaluated by investigators of the Municipal Police of Madrid. Currently the SURVANT system is deployed in the premises of the Municipal Police of Madrid and it is tested in an operational environment.
The result is replicable. The framework used for the deployment are updated to the state of the art. The only hardware requirement is related to the GPU used for the extraction and detection of objects from video footages.
