SimpliSafe is Solveing the Passersby Problem with AI Image Analysis & Loka

SimpliSafe, the US market leader in home security, offers a proprietary range of networked cameras, smart locks and wireless alarm systems, all remotely monitored around the clock by human professionals. These human agents spend thousands of hours reviewing video to separate harmless passerby events from legitimate threats—a process that significantly burdens resources and increases costs.

Loka is helping SimpliSafe harness advanced generative and agentic AI to move beyond reactive alerts toward 100%-accurate AI event detection—a paradigm-shifting solution that could benefit the entire $30B home security industry. Reducing false alerts would allow SimpliSafe to lower operational costs, improve customer satisfaction and scale new services without increasing staff.

Simplisafe’s hybrid security system combines the efficiency of AI image analysis with the confidence of human oversight. Their current baseline AI assesses a constant stream of events, from passersby to prowlers, that occur within range of their 8 million monitoring devices.

Roughly nine out of ten of these events are routine. A high volume of false-positive alerts not inflates operational costs and diverts valuable human resources from genuine security threats. This persistent bottleneck, known as “The Passerby Problem,” brought SimpliSafe to Loka for help refining their threat-assessment process with advanced AI.

Along with ensuring no interruptions to SimpliSafe’s award-winning service while testing, Loka’s challenge was to develop a system that could accurately distinguish between routine passerby activity and potential security anomalies while maintaining the high security standards that have made SimpliSafe the leader in the home security industry.

Loka’s solution architecture integrated three core components: feature extraction using object detection and tracking algorithms, contextual analysis through scene understanding capabilities provided by Amazon Nova models and comprehensive scene summarization leveraging both traditional AI/ML models and generative AI.

Loka’s system classifies events using the first five seconds of video footage and iteratively improves classification accuracy as more frames become available. Agentic workflows not only accurately classify false positives versus real threats but also log and notify human staff based on the incident detected.

Our implementation combines multiple complementary technologies including RT-DETR for object detection, ByteTrack for tracking, optical flow analysis for movement detection and vision language models for automated zone classification—all supported by AWS infrastructure.