Jul 05, 2023
No image
Completed
Video Annotation for Data Analytics Company
<$5,000
Less 1 month
United States, San Jose
10+
Service categories
Service Lines
IT Services
Domain focus
Technology
Subcategories
IT Services
Outsourcing
Challenge
Hiring annotation specialists who could understand the standard automobile classification.
Specialists who had knowledge and experience in building complex computer vision models.
Annotating images from videos under different lighting, weather and erratic traffic volumes required special skills.
Hiring annotation specialists who could understand the standard automobile classification.
Specialists who had knowledge and experience in building complex computer vision models.
Annotating images from videos under different lighting, weather and erratic traffic volumes required special skills.
Solution
Huge volumes of vehicle and pedestrian images from live as well as historical traffic video feeds, from across major cities in the US and Canada were identified, categorized, and labeled.
A workflow was planned after an initial assessment of vehicle and pedestrian images in pre-recorded traffic videos and live video streams, to expedite the labeling process.
Input Data in the form of video footage were received as pre-recorded videos and URLs to live video streams.
Pre-recorded videos were uploaded to OneDrive city wise.
For live videos, human annotators securely accessed the VPN using pre-provided credentials to log into the City’s traffic camera network.
Labeling and segmentation were done as per the following norms
Huge volumes of vehicle and pedestrian images from live as well as historical traffic video feeds, from across major cities in the US and Canada were identified, categorized, and labeled.
A workflow was planned after an initial assessment of vehicle and pedestrian images in pre-recorded traffic videos and live video streams, to expedite the labeling process.
Input Data in the form of video footage were received as pre-recorded videos and URLs to live video streams.
Pre-recorded videos were uploaded to OneDrive city wise.
For live videos, human annotators securely accessed the VPN using pre-provided credentials to log into the City’s traffic camera network.
Labeling and segmentation were done as per the following norms
Results
Vehicles were labeled by their category – model name, the color of the vehicle, and direction of the vehicle.
Objects are classified into 14 categories – 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 labeled.
Any ambiguity on the vehicle due to poor light or weather conditions was re-validated by the client.
Line-based technique was used to uniquely count vehicles and other objects.
Vehicles were labeled by their category – model name, the color of the vehicle, and direction of the vehicle.
Objects are classified into 14 categories – 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 labeled.
Any ambiguity on the vehicle due to poor light or weather conditions was re-validated by the client.
Line-based technique was used to uniquely count vehicles and other objects.