Inspiration

Documenting and analyzing a crime scene is very tedious and difficult task. There are many things that hinder a crime scene investigator to properly do their job.

First off, photographs are a common method to document pieces of evidence in a crime. However, often times, disjointed pieces of imagery do not give the investigators the full picture. There is a possibility where a few photos were taking at the crime scene and they require closeups of pieces of evidence that are no longer available to them.

What it does

Detecto Mode is a mobile Augmented Reality (AR) crime scene annotation tool that allows investigators to spatially map out crime scenes and document pieces of evidence. This tool allows a crime scene investigator to:

  • Spatially map the environment in real time, using AR
  • Collaborate with other crime scene investigators to place notes, highlight important pieces of evidence in AR.
  • Use collected data points from notes and spatial mapping be sent to the cloud, to be processed at the police station.

How we built it

  • ARCore
  • Google Cloud API
  • C#
  • Unity Engine

Challenges we ran into

During this hackathon, we were using a lot of technology such as spatial mapping (photogrammetry) and networking. There were are a lot of problems when it came to setting up these technologies to work into Unity.

Accomplishments that we're proud of

We were able to successfully combine two technologies that we as a team were completely unfamiliar with. In addition, we also made a polished user interface for the final product.

What we learned

An important lesson we took away from this hackathon is to spend time understanding and quantifying a problem. Doing proper research will help inform design decisions. In addition, we also learned that time management is a key part of being able to complete a project on time. As a team, we tried to track progress and set milestones during development of the software we were making.

What's next for Detecto Mode

We would explore the possibility of using technology such as Computer Vision/Machine Learning to have the software auto tag points of evidence. In addition, we would want to create a backend system that would parse the data collected by the crime scene investigators and create useful graphs and visualizations.

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