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More and more law enforcement agencies worldwide are using video surveillance to extend their monitoring and forensic capabilities. Video camera networks are growing exponentially, from cameras embedded in “smart” streetlamps to doorbell video cameras (for which private citizens may opt to share video feeds as evidence and intelligence with local police departments), the combination of video resources from private individuals and businesses and public infrastructure amount to an extensive system for real-time situational monitoring and evidence collection for post-incident investigations. Even without the significant increase in global surveillance cameras (Omdia estimates that by 2022 the number of installed cameras will surpass 1 billion), police are already struggling to actively monitor feeds in real-time and forensically review high volumes of video evidence: These are major challenges for resource-strapped police departments of all sizes.
Deep learning based video content analytics software helps overcome these obstacles, by processing video to identify, categorize and index the objects in video footage (such as clothing, bags, vehicles, animals, and other items). Ultimately this drives mission critical analytics for law enforcement enabling them to:
- Easily search and comprehend video for accelerated investigations based on object classification and tracking, face recognition, and license plate recognition
- Attain situational awareness with real-time alerts, empowering officers to respond quickly to developing situations
- Collect trend data and use it to set forth preventative measures for public safety
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