OCST AI Research Center

OCST AI Research Center

Real-Time Object Tracking

Intelligent Vision System for Logistics and Manufacturing

Overview

Modern manufacturing and logistics operations require precise, real-time visibility of materials, products, and assets as they move through facilities. Traditional tracking methods using barcodes or RFID face limitations in coverage, reliability, and cost.

Our Real-Time Object Tracking Solution uses computer vision and AI to provide continuous, contactless tracking of objects throughout industrial environments, enabling smarter automation and operational insights.

The Problem

Existing tracking systems struggle with:

  • Coverage Gaps: Barcode/RFID requires specific scanning points, creating blind spots
  • Speed Limitations: Manual scanning creates bottlenecks in high-throughput operations
  • Cost: RFID infrastructure is expensive for large-scale deployment
  • Reliability: Occlusion, orientation, and environmental factors affect accuracy
  • Integration: Difficult to integrate with existing automation systems

Our Approach

Vision-Based Multi-Object Tracking

We leverage cutting-edge computer vision algorithms to track objects continuously without physical tags:

Core Technologies:

  • Deep learning-based object detection and classification
  • Multi-object tracking (MOT) algorithms for identity preservation
  • Re-identification (ReID) models for tracking across camera views
  • Predictive modeling for handling occlusions and temporary losses

Tracking Capabilities:

  • Simultaneous tracking of hundreds of objects
  • Cross-camera tracking across facility zones
  • Velocity and trajectory prediction
  • Dwell time and path analytics

System Architecture

Comprehensive Tracking Pipeline:

  1. Multi-Camera Network: Strategically positioned cameras for full facility coverage
  2. Object Detection: Real-time identification of tracked items
  3. Feature Extraction: Unique visual signatures for each object
  4. Track Association: Maintaining object identities across frames and cameras
  5. Data Fusion: Integration with other sensor inputs (weight scales, gate sensors)
  6. Analytics Engine: Real-time insights and anomaly detection
  7. API Integration: Data delivery to warehouse management or MES systems

Key Features

  • Contactless Tracking
    No tags, markers, or manual scanning required.

  • Real-Time Performance
    Sub-second latency for time-critical operations.

  • Scalable Coverage
    Easily expand to cover entire facilities with additional cameras.

  • Robustness
    Handles occlusions, lighting variations, and complex backgrounds.

  • Historical Analytics
    Complete trajectory history for process optimization.

Use Cases

Warehouse & Logistics

  • Inbound/outbound verification and tracking
  • Putaway and picking operation monitoring
  • Pallet and container tracking
  • Loading dock management
  • Inventory reconciliation

Manufacturing

  • Work-in-progress (WIP) tracking through production stages
  • Assembly line monitoring
  • Material flow optimization
  • Cycle time analysis
  • Bottleneck identification

Distribution Centers

  • Cross-docking operations
  • Sortation verification
  • Route optimization
  • Throughput analysis

Business Benefits

Operational Efficiency:

  • Reduced manual scanning and data entry
  • Faster throughput with automated verification
  • Minimized search time for misplaced items

Accuracy & Visibility:

  • Real-time inventory accuracy
  • Elimination of tracking blind spots
  • Enhanced traceability and compliance

Data-Driven Optimization:

  • Detailed operational analytics
  • Process bottleneck identification
  • Resource allocation optimization
  • Performance benchmarking

Cost Savings:

  • Reduced labor costs for tracking activities
  • Lower infrastructure costs vs. RFID
  • Decreased inventory discrepancies and losses

Technical Specifications

Performance Metrics:

  • Tracking Accuracy: >98% object identity preservation
  • Detection Rate: >99% for target object classes
  • Processing Speed: Real-time at 30+ fps per camera
  • Scalability: Support for 50+ cameras per system

System Requirements:

  • IP cameras (resolution: 1080p minimum, 4K recommended)
  • GPU-accelerated compute servers (edge or centralized)
  • Network infrastructure (1 Gbps minimum)

Integration:

  • RESTful APIs for WMS/MES integration
  • Standard protocols (HTTP, WebSocket, MQTT)
  • Database connectivity (SQL, NoSQL)
  • Dashboard and visualization tools

Deployment Options

Edge Deployment:

  • On-premise processing for low latency and data privacy
  • Suitable for facilities with local IT infrastructure

Hybrid Architecture:

  • Edge detection with cloud-based analytics
  • Balances performance with advanced analytics capabilities

Custom Configurations:

  • Tailored to specific facility layouts and requirements
  • Integration with existing automation systems

Implementation Roadmap

  1. Site Assessment: Facility walkthrough and requirement gathering
  2. Pilot Installation: Limited deployment to validate coverage and accuracy
  3. Model Customization: Training on specific object types and facility conditions
  4. Full Deployment: System-wide rollout with comprehensive coverage
  5. Integration: Connection to WMS, MES, or analytics platforms
  6. Optimization: Continuous improvement based on operational feedback

Why This Solution?

  • Proven Technology: Based on state-of-the-art computer vision research
  • Production-Tested: Deployed in real-world logistics and manufacturing facilities
  • Flexible: Adaptable to diverse object types and operational environments
  • ROI-Focused: Measurable improvements in efficiency and accuracy

This solution is designed for organizations seeking to modernize material tracking, enhance operational visibility, and leverage AI for competitive advantage.

Next Steps

Interested in seeing this technology in action?

👉 Schedule a demo to see how real-time tracking can transform your operations.

Case studies, technical white papers, and ROI calculators available upon request.