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BPM

Annotating pre-recorded and live video streams provide accurate training data to power machine learning models for a California based data analytics company

Annotating pre-recorded and live video streams provide accurate training data to power machine learning models for a California based data analytics company Banner

Client Profile

An integrated data analytics company from San José’s, CA providing solutions to government agencies in the energy, water and communications arena. To assist Department of Civil and Environmental Engineering, they were developing a machine learning solution to predict traffic congestions, prevent traffic collisions and improve road planning by better estimating transit demand.

Business Need.

Use the widely deployed traffic camera feeds, pre-recorded and live video streams, to produce directional counts of traffic users to understand study and assess traffic congestion and plan lane movement accordingly. The client was looking to identify, categorize and label thousands of vehicles based on turning movement, direction of approach and mode; as a preparatory step towards its video annotation activities.

They partnered with Hitech in the technology development venture by studying and labeling vehicle images in pre-defined criteria. The labeled images were further to be used to train client’s machine learning models and evaluate if video analytics can detect queues, track stationary vehicles, and tabulate vehicle counts from live video feeds.

Challenges.

Solution.

Identified, categorized and labelled hundreds of thousands of vehicle and pedestrian images, from both live as well as historical traffic video feeds, from across major cities in US and Canada. Human annotated images were further used as extensive training data to be fed into machine learning models.

Approach.

After initial assessment of vehicle and pedestrian images in pre-recorded traffic videos and URLs to live video streams, team of data annotators at Hitech documented a workflow to expedite image labeling. The images were annotated through a five-step process:

Technology Used

Secure Login to City’s traffic camera network through VPN using pre-provided credentials

Business Impact.

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