Technologies : Machine Learning, TensorFlow & Python
Category Name : Machine Learning (ML) Solutions

Client Requirement


The client wanted a traffic monitoring system that enabled to detect different vehicle types swiftly and seamlessly. This traffic monitoring system and automated solution needed to sense Humans, Bicycle, Van, Sedan, Buses and diverse types of other vehicles. Further, the client also needed to get the vehicle number from the vehicle number plate. The idea was to enable traffic systems to tailor their vehicle traffic monitoring and tracking with search functionalities as per the required criteria or preferences.

The client needed the below specifications through the Traffic Monitoring System project developed along with the critical and all-inclusive data analytics reports:

  • Vehicle classification concerning van, bus, pedestrian, sedan, SUV, and bicycle
  • The location, date and time-wise sorting of overall traffic with tracking of data
  • Number of vehicles coming from north, south, east and west sides for traffic insights
  • The vehicle turning directions to understand the traffic conjunction for control
  • Suspicious number plates detection, notifications, and alerts through the system
  • With all the information exported in CSV and XML formats for better access

Key Challenges


The biggest challenge was that the client needed a strong and well developed ML solution which can work on global-basis. The objective was to offer better traffic monitoring and tracking by detecting vehicle types, the number of vehicles coming and going in from several directions, junctions as well as different locations.


Some of the key challenges that were overcome by developing a powerful solution were:

  • A robust and scalable machine learning model was built utilizing commanding algorithms along with technologies like TensorFlow and Python.
  • The system has been taught and made intelligent using millions of images to achieve precision.
  • The ML solution runs on both live streaming and recorded videos that can add value to the classification of traffic analytics system.
  • The current system is achieving more than 95% of accuracy with rain, shadow, and different weather conditions.
  • For number plate detection we used Machine learning model which works best with Australian and US number plates but having difficulty in some of the countries where number plates can be in different fonts, shapes, and languages.
  • We even built a unique OCR model to get the vehicle number automatedly from vehicle number plate. Along with it, we had our separate C++ algorithm for OCR creation.


  • Technostacks’ team of machine learning and other advanced technology development groups created the assorted system modules that can capture analytics of traffic at different junctions.
  • We developed the machine learning model in python, tensor flow and produced datasets to detect vehicle type and category in the day, night and furthermore on the rainy days.
  • As client was looking for a global solution, our specialized technology, data intelligence and analytical teams came up with a solution to use AWS recognise API.
  • AWS recognise API offers number plate detection and OCR for all the countries and types of number plates. We even created an OCR model to get the vehicle number automatedly detected from vehicle number plates.
  • Our team could perceive and quickly identify vehicle categories, the number of vehicles coming and going in from altered directions using this solution.



Technostacks’ programming, advanced development, and analytical expert teams developed a machine learning model which can detect different vehicle types which includes vehicles (bicycle, van, sedan and buses) as well as humans. The solution currently runs on both live streaming and recorded videos providing a precise classification of traffic analytics and data reporting. The system is consistently trained with millions of images to attain absolute information accuracy for further data intelligence solutions.