A.I Emotion Detector

 

This is an ongoing project using Tensorflow A.I platform made by google, The aim of this CNN A.I is to be able to run live camera footage from a Trade show or Marketing event.

The A.I detects the presence of Human faces and then runs the CNN network on the detected cropped face to determine what the human emotional state is. In the example image The network is being run on Video comedy footage as a test example, The idea for deployment is to have a camera mounted above the exit door’s at large trade shows or events.

As people walk under the camera the A.I creates an emotional state profile for each person & then outputs whether the person leaving has positive emotion or negative, This data is then fed into a CSV file database allowing the event organizer to build in realtime feedback on the event.

This type of deep data is like gold dust for automated feedback which can then be run into a interactive dashboard giving graphs for positive feedback or negative, The final dashboard then generates graphs and visual statistics allowing to evaluate the success of the event.

This can run on edge hardware directly at the event location or streamed to the cloud from a dumb edge device just streaming video to the AI on-line.

 

This project has expanded to also allowing gender classification of humans detected, & age prediction for more detailed analytics on attendee’s.

This Project Included

• Tensorflow AI
• Python
• CNN A.I Deep learning creating
• Custom trained A.I Model and Dataset

Here’s a short video clip:

A.I People Counter

 

This is an ongoing project using Tensorflow A.I platform made by google, The aim of this RCNN A.I is to be able to run in live camera footage from a Trade show or Marketing event.

The A.I detects the presence of People and then runs the RCNN network on the detected people as they cross the R.O.I Line (Red line in image), As people pass this counting point the People counter in the top left is incrimentaly increased. These results are fed into a CSV file adding what direction of travel is, Estimated speed. The network is being run on Videosurvalance camera in a high street environment.

The idea for deployment is to have a camera mounted above the exit door‘s at large trade shows or events, as people walk under the camera the A.I counts the attendee’s & then outputs the result.

This data is then fed into a CSV file database allowing the event organizer to build in realtime feedback on the event.

This type of deep data is like gold dust for automated feedback which can then be run into a interactive dashboard giving  graphs for attendee numbers, Thsi Ai is designed to be teamed with out other Gender,Emotion,Age AI to give a complete event classification system for analytics (to create a demographic of end user trends).

The final dashboard then generates graphs and visual statistics (Analytics) allowing to evaluate the event attendee’s.

This can run on edge hardware directly at the event location (High end hardware) or streamed to the cloud from a dumb edge device just streaming video to the AI on-line.

 

 

This project is related to our computer vision classification system.

This Project Included

• Tensorflow AI
• Python
• RCNN A.I Deep learning
• Custom trained A.I Model and Dataset

Here’s a short video clip:

A.I Age Classification

 

This is an ongoing project using Tensorflow A.I platform made by google, The aim of this DNN A.I is to be able to run in live camera footage from a Trade show or Marketing event.

The A.I detects the presence of Human faces and then runs the DNN network on the detected cropped face to determine what the human age is. In the example image The network is being run on Video comedy footage as a test example.

The idea for deployment is to have a camera mounted above the exit doors at large trade shows or events, as people walk under the camera the A.I creates a age classification profile for each person & then outputs the result, This data is then fed into a CSV file database allowing the event organizer to build in realtime feedback on the event.

Clearly this could be used within retail environments as a means to build analytics mixed with our other AI platforms to generate data on specific areas of the outlet to give feedback on the age group most interested in that product line. This data could be used to determine new lines or for targeted sales campaigns.

This type of deep data is like gold dust for automated feedback which can then be run into an interactive dashboard, giving graphs for age specific behavior. For example running this A.I in a retail environment mixed with our human counting system would allow understanding which product is popular with a specific gender group (As part of another of our experiments this also can be joined with gender classification to create a demographic of end user trends), The final dashboard then generates graphs and visual statistics (Analytics) allowing to evaluate the popularity of that area.

This can run on edge hardware directly at the event location (High end hardware) or streamed to the cloud from a dumb edge device just streaming video to the AI on-line.

 

This project is related to our computer vision classification system.

This Project Included

• Tensorflow AI
• Python
• DNN A.I Deep learning
• Custom trained A.I Model and Dataset

Here’s a quick video clip showing the AI in action.

A.I Gender Classification

 

This is an ongoing project using Tensorflow A.I platform made by google, The aim of this DNN A.I is to be able to run in live camera footage from a Trade show or Marketing event.

The A.I detects the presence of Human faces and then runs the DNN network on the detected cropped face to determine what the human gender is. In the example image The network is being run on Video comedy footage as a test example.

The idea for deployment is to have a camera mounted above the exit door‘s at large trade shows or events, as people walk under the camera the A.I creates a gender classification profile for each person & then outputs the result.

This data is then fed into a CSV file database allowing the event organizer to build in realtime feedback on the event.

This type of deep data is like gold dust for automated feedback which can then be run into a interactive dashboard giving  graphs for male or female behavior, for example running this A.I in a retail environment mixed with our human counting system would allow understanding which product is popular with a specific gender group (As part of another of our experiments this also can be joined with age classification to create a demographic of end user trends).

The final dashboard then generates graphs and visual statistics (Analytics) allowing to evaluate the popularity of that area.

This can run on edge hardware directly at the event location (High end hardware) or streamed to the cloud from a dumb edge device just streaming video to the AI on-line.

 

This project is related to our computer vision classification system.

This Project Included

• Tensorflow AI
• Python
• DNN A.I Deep learning
• Custom trained A.I Model and Dataset

Here’s a short video clip: