info@habiledata.com
Read Disclaimer
⟨

Annotation of Live Video Streams for Traffic Management and Road Planning

Annotation Apr 20th, 2024
Annotation of Live Video Streams for Traffic Management and Road Planning
Scroll Down

Client Profile

The Company: An Integrated Data Analytics Company

Industry: Data Analytics

Company Headquarters: San José, CA, USA

The Objective:

The client a data analytical company providing solutions to government agencies was looking to categorize and label huge number of vehicles based on movement like approach, turning movement etc for the following reasons:

  • To assist department of Civil and Environmental Engineering in developing proper road plans for smooth traffic movement.
  • To develop machine learning solution predicting traffic related issues like congestion, accident prevention, better road planning, lane movement etc.
  • To train client’s machine learning algorithms through labelled videos with added metadata to make the objects identifiable in videos.
  • To develop models and technology to evaluate if video analytics can track traffic situation from live video feeds.

Therefore, the client partnered with HabileData to label vehicle images in pre-defined criteria.

The Challenges:

  • Hiring skilled workforce with experience in building complex computer vision models related to standard automobile classification.
  • Skilled annotators who could annotate images from videos under different lighting, weather and erratic traffic volumes.
  • Dividing the workforce into shifts to train massive amount of data.
  • Expertise in labelling images from traffic videos as often the videos became unsteady with blurred images, obstructions etc.
  • Choosing the right annotation method and accurately labelling the data required to train the machine how to recognize them visually, like a person.
  • Managing the complex process of annotating videos or multi-frame data.
  • Counting pedestrians and bicycles from in-pavement loops not differentiated for directions of movement (going straight and turning right).

HabileData’s Solution

  • Workflow was planned to categorize and label huge volumes of vehicle and pedestrian images from live as well as video feeds from across major cities in US and Canada.
  • The annotated images were used as training data for machine learning models.
  • A well-documented 5-step process helped successfully annotate image labelling
  • Data was sourced in two forms:
    • Pre-recorded videos
    • Live video streams
  • Credentials to log into the City’s traffic camera network gave annotators access to live video streams.
  • Labelling and segmentation was done as per the following norms:
    • Vehicles labelled by – category, model name, colour and direction of vehicle
    • 14 categories included – Car, SUV, small truck, medium truck, large truck, pedestrian, bus, van, group of people, bicycle, motorcycle, traffic signal-green, traffic signal yellow, and traffic signal-red.
    • Vehicles were classified tagged and segmented by turning movement or by the direction of approach
    • Obstructed vehicles were not labelled
    • Any ambiguity on the vehicle due to poor light or weather conditions were re-validated by the client
  • Count of vehicles in individual lanes was done through line-based technique.
  • State of the line changed from unoccupied to occupied and then back to unoccupied increasing the count of said line.
  • A demarcation line (red line) was used to demarcate small vehicle that was not labelled.
  • A team of senior auditors audited around 10% of the annotated images.
  • Any anomalies or deviations in the data were used for training purpose.
  • Any annotation found erroneous was taken up for re-labelling.
  • City wise segregation helped in uploading the labelled images on OneDrive.
  • Report listing number and types of vehicles annotated was generated for record purpose.

Business Impact

  • Huge volumes of training data to power machine learning
  • A dashboard of directional traffic volumes providing live data and alerts
  • Video analytical solution for traffic developed

Value Addition

Annotating pre-recorded and live video stream of vehicles provided training data for machine learning models for a California based data analytics company helped managing traffic efficiently.

Go to Top