Ethical and fair vision based safety and security
Modern systems still rely heavily on human operators to monitor real-time CCTV footage, a process vulnerable to fatigue, distraction, and human error. While AI-driven systems promise real-time anomaly detection and automated alerts, they face major roadblocks, low accuracy and poor robustness, especially with respect to ethnicity bias, leading to false alarms and missed events. Part of the solution is a need for large, high-quality, relevant training datasets. Gathering this data from real-world sources is slow, expensive, and fraught with privacy, diversity, and provenance issues.
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The solution: unlimited, privacy complaint data from Synthera Chameleon™
Why Traditional Approaches Fall Short
Relying on real-world footage to train AI surveillance models introduces serious challenges. Collecting video data at scale is costly, time-consuming, and often violates privacy laws. Even when data is available, it may lack diversity in edge cases, fail to capture rare but critical scenarios, or be inadequately annotated. As a result, AI systems trained on such data are biased, brittle, and unreliable in live environments.
How Synthera™ Solves It
Synthera’s™ platform enables the rapid creation of large, diverse, and photorealistic synthetic datasets, which are completely free from privacy concerns. With fine-grained control over scenes, behavior, environments, and edge cases, data scientists can simulate thousands of realistic scenarios, from normal daily routines to rare or suspicious behaviors. Our end-to-end solution ensures data quality, consistency, and relevance, dramatically reducing time-to-deployment for AI surveillance models.
Use Case Examples
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Outdoor surveillance for car parks and public spaces
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Smart building occupancy and energy usage tracking
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Intrusion detection in home and smart home environments
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Anomalous behaviour detection
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Crowd detection and monitoring
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Pedestrian Traffic flow
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Abandoned vehicle detetcion
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Fly tipping
