The goal of this project was to explore how to merge or fuse together real time video streams. It's about exhausting a scene down to colors and movement and repurpose this into an abstract representation. When info has been stripped away, the scenes can be merged together. First two networks are both a direct representation of the flow of traffic at different locations. The third data network is an attempt at trying to merge and fuse these two together.

This experiment grabs two random street cams around Seoul. Traffic from each camera is analyzed and the color of each car registered is extracted and displayed as a small part of a bigger network. Traffic cameras are passive and running 24/7. As a result it will give different results based on what time it is. The third network represents the merge of the different scenes.

It's a proof of concept of trying to analyze and gather info from live streams. To develop this further I would've done this with a camera where visitors can see themselves / their surroundings being analyzed and taken apart and "merged" with visitors in other locations. As this is only a proof of concept I chose to analyze an already available live stream with a steady, continuous flow of movement. The live streams are taken from spatic.go.kr.

Unfortunately the tracker is not that accurate. It's using contour / difference tracking via. Open CV where it looks for pixels that differ from a set background image. As a result it will also pick up light differences such as the headlights of the car. I realised halfway through the project that a more effective / accurate approach would be to use a machine learning model with TensorFlow trained on specific scenes. If I had to redo this project I would've gone with this approach instead. For example, with Google's "Teachable Machine" project.