How would you design an end-to-end surveillance system that automatically detects and alerts security personnel to ‘anomalous events’ (e.g., break-ins, fainting, or prohibited movements) in a large shopping mall?
Answer
A surveillance anomaly detection system captures video streams, preprocesses them into clips, and uses a deep learning model, typically a pretrained video backbone plus a lightweight anomaly scoring head, to identify unusual behavior.
It operates in a semi-supervised setup trained on normal data, runs in real time with sliding windows and temporal smoothing.
The system also includes alerting, monitoring, and a human-in-the-loop feedback loop for calibration and retraining.
Data Ingestion & Preprocessing: Capture real-time video streams from multiple cameras. Preprocess by resizing frames and normalizing pixel values.
Model architecture:
(1) Feature Extraction: A 2D CNN (like EfficientNet) extracts spatial features. To capture motion, we use Optical Flow or a 3D CNN (I3D) or a Video Transformer (Video Swin Transformer or TimeSformer) to look at blocks of frames together.
(2) The “Normal” Model: We train an Autoencoder or a Generative Adversarial Network (GAN) on months of “normal” mall activity.
(3) Detection Logic: When the model sees something new, its “reconstruction error” will be high. If the error exceeds a set threshold, it is flagged as an anomaly. Use the validation dataset for threshold calibration.
Alerting & Visualization: Generate real-time alerts. Send anomalous frames for human operators to review. Implement a Human-in-the-Loop system where guards can click “Not an Anomaly.”
System Considerations:
(1) Scalability: Use edge devices for preliminary processing to reduce bandwidth; cloud processing for heavy computation.
(2) Latency: Optimize frame rate and model inference time to enable near real-time detection.
(3) Evaluation: Test using precision, recall, F1-score, and monitor false positives/negatives.
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