Yes, vector search can be used to detect loitering, crowding, or abnormal behavior in video analytics systems. The approach relies on converting raw data (e.g., video frames, sensor inputs) into numerical representations called vectors, which capture patterns like movement trajectories, object density, or spatial relationships. By comparing these vectors to predefined patterns or identifying outliers, developers can flag unusual behavior in real-time or post-analysis.
To implement this, a system might first process video streams using computer vision models to extract features like object positions, speeds, or group formations. For example, a person loitering might be represented as a vector encoding their location over time, with low velocity and repeated circular motion. Crowding could be detected by analyzing spatial density vectors—such as clusters of people in a small area—using similarity searches against thresholds. Vector search engines like FAISS or Milvus enable efficient comparison of these high-dimensional vectors, allowing developers to query for patterns (e.g., “find all instances where 10+ people are within a 5m radius”) or detect deviations from normal behavior. For abnormal behavior detection, techniques like k-nearest neighbors (k-NN) or clustering algorithms (e.g., DBSCAN) can identify vectors that fall outside typical distributions.
A practical example: In a retail setting, a security system could track customer movements using pose estimation models to generate trajectory vectors. A vector search could flag loitering by checking for trajectories with low average speed and high positional variance over a 5-minute window. Similarly, crowding detection might involve segmenting the floor plan into grid cells, converting cell occupancy counts into vectors, and triggering alerts when density exceeds a predefined threshold. Challenges include balancing accuracy with computational efficiency—high-resolution video or complex scenes may require optimizing vector dimensionality or using approximate nearest neighbor (ANN) search. Privacy considerations also arise, as raw video data is often converted to anonymized vectors to avoid storing identifiable information. Developers must fine-tune models and thresholds to minimize false positives while ensuring the system adapts to varying environments.