Computer Vision • Smart City

[REDACTED] Smart City Infrastructure

Multi-Vehicle Detection & Real-time Tracking

Client:Municipal Infrastructure Provider
Timeline:14 months (2023-2024)
Team:Computer Vision Team + Infrastructure Engineers
YOLOOpenCVDeep LearningMulti-Object Tracking (MOT)PythonEdge ComputingTraffic Intelligence
System Architecture

The Challenge

Managing highway traffic and city infrastructure requires real-time, high-accuracy data on vehicle density, classification, and movement patterns. Legacy systems often struggle with:

  • Low accuracy in vehicle detection during varied weather and lighting conditions
  • Inability to track individual vehicles across multiple frames in high-density traffic
  • Limited classification capabilities (e.g., failing to distinguish between types of commercial vehicles)
  • High latency in processing video streams for real-time decision support
  • Lack of automated direction and velocity tracking for safety analysis

The Solution

Engineered a robust multi-vehicle detection and tracking system using state-of-the-art deep learning architectures. The system provides real-time traffic intelligence with high-accuracy individual vehicle monitoring.

Core technical implementation:

  • YOLO-based object detection optimized for high-speed vehicle identification
  • Advanced Multi-Object Tracking (MOT) algorithms for consistent ID assignment across frames
  • Automated vehicle classification including axle/wheel count detection
  • Real-time direction of movement and velocity estimation
  • Edge-optimized deployment for low-latency processing at the camera site
  • Robust performance in diverse environmental conditions (rain, night, fog)

Lexer System's Approach

1

Detection Model Optimization

Customized YOLO architectures to achieve the optimal balance between inference speed and detection accuracy for high-speed highway scenarios.

2

Advanced Tracking Logic

Implemented specialized tracking filters to handle occlusions and maintain unique vehicle IDs as they move through complex traffic patterns.

3

Feature Extraction & Classification

Developed additional CNN layers for fine-grained vehicle classification, detecting specific attributes like commercial markings and wheel counts.

4

Edge Deployment & Scalability

Optimized models for deployment on edge computing hardware, reducing bandwidth requirements and enabling real-time alerts for traffic management centers.

Results & Impact

97.2%
Detection Accuracy

High-precision vehicle identification

95% ID Persistence
Tracking Consistency

Maintaining unique IDs through dense traffic

<30ms
Processing Latency

Real-time performance at the edge

12+ Classes
Classification Depth

From motorcycles to multi-axle heavy transport

Technical Highlights

Real-time MOT (Multi-Object Tracking)

Highly consistent tracking logic that handles overlaps and temporary occlusions with high reliability.

High-Speed Bounding Box Detection

Optimized inference pipeline that accurately captures vehicles moving at 120km/h+.

Environmental Robustness

Preprocessing layers and model training specifically designed for reliable performance in low-light and adverse weather.

Lessons Learned

  • Edge processing is essential for large-scale urban deployments to manage data costs
  • Handling occlusions (one car blocking another) is the most difficult part of tracking at scale
  • Regular model retraining on specific local traffic patterns significantly improves classification accuracy

Next Steps

  • Integrate license plate recognition (LPR) for automated toll and safety systems
  • Develop predictive traffic flow models based on real-time detection data
  • Expand to pedestrian and cyclist detection for comprehensive urban safety monitoring

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