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AI for Person Tracking Across Multi-Camera Security Systems

Project’s Main Goal

The primary goal of the Person Re-Identification System project is to develop an intelligent, multi-modal AI-powered system that enhances security and surveillance by accurately identifying and tracking individuals across multiple camera feeds. This solution leverages face recognition, gait analysis, and shoe detection to achieve high accuracy in diverse environments. The system is designed to streamline monitoring operations, ensure reliable identity verification, and optimize security workflows through advanced automation, scalable architecture, and intelligent data management. 

Industry

Security & Surveillance Solutions

Location

Saudi Arabia

Tech Team

Project Manager | Team Lead  | AI Developer | Data scientist | Network Engineer | DevOps Engineer

Business Tasks the Client Wanted to Address

Seamless Person Re-Identification Across Cameras

  • Build an AI system to accurately recognize and track individuals across multiple entry and exit points, even with changing appearances or partial visibility.

Enhanced Security and Access Control

  • Automate identity verification at critical checkpoints to strengthen security and reduce reliance on manual monitoring.

Scalable, Privacy-Preserving Identity Database

  • Maintain a centralized, high-performance vector database for storing and matching thousands of identities while ensuring privacy compliance.

Real-Time Monitoring and Operational Efficiency

  • Provide real-time alerts, reduce manual review time, and deliver high-quality embeddings for analytics and long-term security planning.

Pitfalls the Client Faced

Inconsistent Person Tracking Across Frames

Existing solutions struggled with re-identifying individuals when body orientation, walking direction, or partial occlusions occurred, leading to unreliable tracking. 

Low-Quality Silhouette and Face Data

Traditional background subtraction and face capture methods often produced noisy silhouettes and poor-quality face images, reducing embedding accuracy.

Lack of Robust Multi-Modal Identity Verification

Systems relied on single-mode recognition (either face or gait), which caused errors in environments where faces were partially visible or obscured.

Scalability and Integration Challenges

Difficulty managing large-scale identity data in vector databases and integrating models into a unified, real-time pipeline hindered deployment.

Our Suggested Solution

Advanced Person Detection 

  • YOLOv10 Integration to detect individuals in real-time with high accuracy and low latency.
  • Pose Estimation using YOLOv10-pose models to analyse body posture and improve tracking reliability.
  • Entry-Exit Line Logic to ensure detection and processing only occur when a person fully enters a defined zone.

Face Recognition & Enhancement

  • InsightFace Embeddings for generating 512D feature vectors for precise identity verification.
  • GFPGAN Face Enhancement to improve low-quality or blurred face images before embedding generation.
  • Vector Database Matching using FAISS for fast and scalable face similarity comparison.

Gait Recognition Pipeline

  • SwinGait Model to extract gait features from silhouette sequences for robust re-identification.
  • High-Quality Silhouettes generated via P3MNet-HQ or RVM, supported by CLAHE and ZeroDCE++ preprocessing.
  • Single Gait Cycle Input to ensure maximum accuracy by feeding only a clean gait cycle to the model.

Shoe and Body Shape Analysis

  • YOLOv10 Shoe Detection to capture footwear details as an additional identity factor.
  • ResNet-50 Shoe Embeddings to create 2048D shoe feature vectors, improving recognition in face-occluded scenarios.
  • Body Shape Features derived from key points (torso width, limb ratios) to strengthen identity verification.

Real-Time Tracking & ID Assignment

  • ByteTrack Multi-Object Tracking for consistent tracking across frames and handling multiple people simultaneously.
  • Cosine + Euclidean Matching logic for more reliable identity assignment by comparing multiple metrics.
  • FAISS Vector Databases for seamless management of face, gait, and fused embeddings, ensuring scalable and fast retrieval. 

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Technical Architecture

Detection and Tracking Layer 

  • YOLOv10-based models for robust person, face, and shoe detection.

  • ByteTrack tracker for multi-object tracking and persistent ID assignment.

  • Entry-Exit line detection to control recognition workflows. 

Face Recognition and Enhancement Layer 

  • InsightFace embeddings for precise 512D face vector generation.

  • GFPGAN for face image restoration and enhancement.

  • FAISS vector DB for real-time face similarity search.

Gait Recognition Layer

  • SwinGait (Swin-T variant) for 3D gait feature extraction.

  • Silhouette generation using RVM or P3MNet-HQ with CLAHE+ZeroDCE++.

  • Gait cycle detection to ensure a single, clean gait sequence per ID.

Footwear and Body Shape Analysis Laye

  • YOLOv10 shoe detection for extracting detailed shoe crops.

  • ResNet-50 embeddings for 2048D shoe feature vectors.

  • Body keypoint extraction for structural identity verification.

Embedding Fusion and Matching Layer

  • FAISS-based databases for face, gait, and combined embeddings.

  • Hybrid similarity scoring (cosine + L2 distance) for improved ID assignment. 
     

  • Intelligent ID management to reduce false positives.

System Integration and Optimization Layer

  • Microservice-based modular design for scalability and maintainability.

  • CUDA acceleration and ONNX runtime for inference optimization.

  • Containerized deployment using Docker for seamless scalability. 

Business Outcomes

Enhanced Security and Monitoring

  • AI-driven person re-identification strengthens surveillance and security workflows. 

  • Real-time tracking and recognition minimize risks of unauthorized access.
  • Improved incident response with accurate and reliable identity verification.

Operational Efficiency and Automation

  • Automated identity matching reduces manual monitoring and human errors.
  • Scalable pipeline enables seamless processing of multiple video feeds.
  • Optimized inference speeds improve overall system responsiveness.

Privacy and Compliance Assurance

  • Encrypted vector databases ensure secure identity data storage.
  • GDPR and data-privacy compliance maintained across all workflows.
  • Strict role-based access control for sensitive identity data.

High Accuracy Multi-Modal Recognition

  • Combines face, gait, and shoe features for robust identification.
  • AI-driven similarity scoring improves ID assignment stability.
  • Enhanced model architecture ensures higher recognition precision.

Seamless System Integration

  • Easy integration with existing CCTV, security, and access systems.
  • Modular pipeline supports flexible deployment across environments.
  • Containerized solution (Docker) ensures portability and scalability.

Future-Ready, Scalable Infrastructure

  • Built with modular microservices to enable future upgrades.
  • Supports adding new recognition features (behavior, attributes, etc.).
  • Scalable design enables enterprise-level, multi-location deployments. 
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