Designing Real-Time AI Algorithms for Unmanned Retail Stores

Algorithm Module Selection and Decomposition

The algorithm pipeline is split into calibration, face, detection & recognition, tracking, and recommendation modules. Calibration combines board‑based and image‑feature matching methods to balance precision and speed. Face recognition supports identity verification and payment, while detection uses a multi‑task model to output bounding boxes, instance masks, keypoints, and embeddings for re‑identification.

The tracking module integrates target tracking, pedestrian re‑identification, and user behavior analysis to generate continuous motion trajectories and detect abnormal events such as falls.

Deep Learning Sample Collection and Data Augmentation

Samples are captured automatically from security, depth, and face cameras, labeled via Suning AI annotation platform, and fed into the Suning Machine Learning platform for training. Data augmentation includes multi‑angle point‑cloud projection and GAN‑based techniques to improve diversity and generalization.

Deep Learning Model Training and Deployment Solutions

Initial training combines public datasets with proprietary labeled data. After obtaining a baseline model, inference on unlabeled samples generates pseudo‑labels that are quickly corrected on the annotation platform, iteratively refining the training set until the optimal model is reached.

Deployment targets NVIDIA GPUs: models are exported to ONNX, then optimized with TensorRT. Pre‑processing (affine transforms, normalization) and post‑processing (NMS, decoding) are moved to CUDA kernels, keeping the entire inference pipeline on the GPU to meet real‑time frame‑rate requirements.

The recommendation module fuses online and offline user behavior, using face‑based identity to link in‑store actions with e‑commerce profiles, enabling precise product suggestions across channels.

Overall, the architecture balances speed and accuracy, ensuring each module meets real‑time constraints while maintaining high precision for calibration, detection, tracking, and recommendation tasks.