Surveillance embeddings are vector representations that capture essential features from visual data (like images or video frames) to enable tasks such as object detection, facial recognition, or activity analysis. The models used to generate these embeddings are typically deep learning architectures optimized for extracting spatial or spatiotemporal features. These models transform raw input data into compact numerical vectors that preserve critical information for downstream tasks, such as identifying individuals, tracking objects, or detecting anomalies.
Convolutional Neural Networks (CNNs) are the most common backbone for image-based surveillance embeddings. Models like ResNet, EfficientNet, and MobileNet are widely used due to their balance of accuracy and computational efficiency. For example, ResNet-50, pretrained on large datasets like ImageNet, is often fine-tuned on surveillance-specific data to generate embeddings for facial recognition or object re-identification. Lightweight architectures like MobileNet are preferred for edge devices, as they reduce computational costs while maintaining reasonable accuracy. For real-time object detection in surveillance footage, models like YOLO (You Only Look Once) or EfficientDet are employed. These models not only detect objects but also generate embeddings that help track entities across frames or cameras by comparing feature vectors.
For video-based surveillance, which requires analyzing temporal patterns (e.g., recognizing activities), 3D CNNs or hybrid architectures are used. Models like C3D (Convolutional 3D) or I3D (Inflated 3D ConvNet) extend traditional CNNs to process sequences of frames, capturing motion and spatial features simultaneously. In cases where re-identifying individuals across different camera angles is critical, specialized models like OSNet or PCB (Part-based Convolutional Baseline) generate embeddings robust to variations in pose or lighting. These models often use triplet loss or contrastive learning during training to ensure embeddings from the same identity are closer in vector space than those from different identities. For facial recognition, FaceNet and ArcFace are popular choices, with ArcFace improving discrimination by optimizing angular margins between embeddings. To balance performance and deployment needs, many systems use techniques like model quantization or pruning to adapt these architectures for real-time inference on edge devices.