Video Task Tracking Tool
Thousands of Hours Footage Become Insights in Seconds
Thousands of Hours Footage Become Insights in Seconds
Vid.Supervisor is a machine learning model that runs over a video to identify and tag behaviours. The goal is to decrease the man-power need to codify a video via a software that has a flexible fit for multiple retail business cases. In the complete solution, human input will be minimum, with only the role of reviewer remaining.
As the monotonous tasks are completed by AI, client team will confirm the automatically assigned tags (improving the algorithm’s accuracy), while client employees can concentrate their energies on work that requires thinking and analysis.
Vid.Supervisor it is ready for use in a variety of retail projects, first easing the creation of codebooks and ultimately reducing the time needed to codify a video by over 90%.
For staff tracking, we will use a people detector to be able to position them at a pre-defined Location at any moment in time (e.g. at workstation). This System will track every Person in frame, displaying a bounding box around them (optional) for easy visualization. It will also assign a unique ID to each (called here a detection ID), for future compatibility.
To extend the capabilities of the people recognition feature, facial recognition will be incorporated to link a person’s activity through any span of time (day, week, year …). Assuming we see a person’s face at some point while they’re in frame, we can retroactively tag their entire time in the frame, meaning video sewing and identification of activities per person is available.
From this process a separate video can be created for each individual staff member/tag/location, containing only the activity of that person/tag/location. This would be sewn together from entire frames in which that person/tag/location appears or from cropped frames containing only that person’s activity/tag/location.
