## Inspiration

Recognizing the importance of time in search and rescue situations, we aimed to develop a system that leverages AI to detect and report incidents more efficiently.

## What it does

Safe Search is an AI-powered multi-faceted search and rescue system that allows search and rescue teams to effectively automate the most time consuming aspects of human recovery. We accomplish this goal through two distinct features: an AI-powered remote camera system to identify those who are lost and an automated call system using AI. This system allows search and rescue teams to more quickly receive information regarding the nature of emergencies and focus their efforts on supporting individuals in need. Where human responders are not available, AI leads the way.

## How we built it

Safe Search utilizes a Raspberry Pi camera system to detect humans and traces of human activity through a pre-trained model. When an incident is identified, the system captures and transmits real-time data, including timestamp, location, and images, to a centralized dashboard. Simultaneously, an SMS alert is sent to rescuers via Infobip. The dashboard, powered by Google Maps integration, displays incident locations, providing a comprehensive overview for quick response.

Additionally, Safe Search's call center employs Twilio and GPT-3.5. This call center efficiently handles incoming calls, extracting essential information such as caller's name, phone number, location, and incident details. The extracted data is then dynamically displayed on the map, facilitating a coordinated and swift response to incidents.

Hardware:
Raspberry Pi for on-board processing.
Night Vision Camera module for image capture.
KIT-HGDRONEK66 for drone base

Software:
Infobip for SMS alerts.
Twilio for call handling.
Pre-trained AI model COCO for human detection.
Google Maps API for incident visualization.
GPT-3.5 for autonomous call center responses.
MongoDB for storing incident information.

## Challenges we ran into

1. Integrating the drone hardware with the Raspberry Pi and ensuring real-time image processing.
2. Developing a seamless communication system for incident reporting to the dashboard and SMS alerts.
3. Ensuring accurate extraction and display of caller information in the autonomous call center.
4. Fried speech controlling unit on drone causing drone losing power to motors

## Accomplishments that we're proud of

1. Successful integration of drone technology for efficient incident detection.
2. Seamless communication between hardware components and the centralized dashboard.
3. Autonomous call center capabilities, enhancing response efficiency.

## What we learned

1. Enhanced understanding of drone hardware integration and real-time image processing.
2. Effective utilization of APIs for geographical visualization.
3. Implementation of AI models for call center automation.

## What's next for SafeSearch

1. Implementing additional AI models for more precise incident characterization.
2. Attaching the Raspberry PI to our drone
3. AI Object Labeling to make pictures easier to understand.

## Research

In 2021, 500,000 people were reported lost while camping, necessitating search and rescue efforts for their location. In Canada alone, 31,334 children and 33,393 adults went missing in 2022. Despite the fact that 77% of people are typically found within the first 24 hours, search and rescue teams are often understaffed and inadequately equipped for the time-sensitive nature of their missions. Safe Search aims to address this gap by offering an AI-powered drone service that empowers front-line defenders with innovative technology.
Search and rescue operations are inherently challenging and unpredictable, with factors like time constraints and difficult terrains significantly influencing mission outcomes. Drones, with their speed, situational awareness capabilities, and detection tools such as thermal cameras, are positioned as valuable assets in improving response times and effectively locating and rescuing individuals. The search and rescue drone market is projected to grow from $3,254.4 million in 2023 to $11,648.1 million by 2033.

## Citations

Aggarwal, N. (2022, March 2). Datawatch: Lost in the woods? Analytics might save your life. RTInsights. <https://www.rtinsights.com/datawatch-lost-in-the-woods-analytics-might-save-your-life/>
Crime, O. of the F. O. for V. of. (2018). Home: Federal ombudsman for victims of crime. [https://victimsfirst.gc.ca/vv/MPI-RPD/index.html#:~:text=According%20to%20the%20RCMP’s%20National,cases%20left%20unresolved%20each%20year](https://victimsfirst.gc.ca/vv/MPI-RPD/index.html#:%7E:text=According%20to%20the%20RCMP%E2%80%99s%20National,cases%20left%20unresolved%20each%20year).
Dotson, R. (2023, June 30). Statistics of getting lost and found. Survival Dispatch. <https://survivaldispatch.com/statistics-of-getting-lost-and-found/>
Drones for search and rescue operations | sar drones. (n.d.). Retrieved November 25, 2023, from <https://www.flytbase.com/blog/drones-for-search-rescue>
Fact. Mr – search and rescue drone market size, share, growth, forecast analysis, by drone type(Consumer/civil, commercial, military) , by product type(Consumer/civil, commercial, military) , by end use vertical (Police & homeland security, others), & region—Global market insights 2023-2033. (n.d.). Retrieved November 25, 2023, from <https://www.factmr.com/report/search-and-rescue-drone-market>
News, A. B. C. (n.d.). Why the first 72 hours in a missing persons investigation are the most critical, according to criminology experts. ABC News. Retrieved November 25, 2023, from <https://abcnews.go.com/US/72-hours-missing-persons-investigation-critical-criminology-experts/story?id=58292638>
## Inspiration

With the recent scare of Hurricane Florence, we wanted to create a system to more quickly identify people needing aid in disaster-stricken areas.

## What it does

Safety Net is an application designed to run in cooperation with a swarm of drones. Search and Rescue drones are already becoming commonplace, and optimizing their capabilities has the potential to save countless lives. Drones that are deployed to an area affected by natural disasters are often sent to find specific missing persons or to investigate infrastructure failures. Safety Net expands the reconnaissance abilities of the drone swarm by identifying any individuals that are possibly remaining in the region. No prior information is needed about the area - the drones simply sweep the affected zones and use Haar Cascades to spot people in need of rescue on the ground. Safety Net then refines the results using IBM-Watson before transmitting its findings to a base-of-operations in the area. From there, a local server fitted with Google's Maps API drops pins wherever people are found, and the operator can quickly view still images of the area to identify people in need as quickly as possible.

## How we built it

We used OpenCV and IBM-Watson as our two visual classifiers. The location data and images are sent to a Node JS server, which communicates with the Google Maps API to display a comprehensive map to the operators.

## Challenges we ran into

None of our 4 team members were too familiar with Node JS or front-end development. Much of our time was spent simply exploring the available functionality.

## Accomplishments that we're proud of

In line with the previous question, we were very proud that we were out of our comfort zones for much of the time, and yet were able to have a finished project with the intended functionality. Although it is not perfect, Safety Net does its job well and could potentially be developed into life-saving software.

## What we learned

As previously mentioned, we learned a lot of new technologies.

## What's next for Safety Net

We'll continue to develop the project, cleaning up the interface and adding reach goals until we're satisfied with the relevance and helpfulness of Safety Net.
## Inspiration

Our inspiration for this project was our experience as students. We believe students need more a digestible feed when it comes to due dates. Having to manually plan for homework, projects, and exams can be annoying and time consuming. StudyHedge is here to lift the scheduling burden off your shoulders!

## What it does

StudyHedge uses your Canvas API token to compile a list of upcoming assignments and exams. You can configure a profile detailing personal events, preferred study hours, number of assignments to complete in a day, and more. StudyHedge combines this information to create a manageable study schedule for you.

## How we built it

We built the project using React (Front-End), Flask (Back-End), Firebase (Database), and Google Cloud Run.

## Challenges we ran into

Our biggest challenge resulted from difficulty connecting Firebase and FullCalendar.io. Due to inexperience, we were unable to resolve this issue in the given time. We also struggled with using the Eisenhower Matrix to come up with the right formula for weighting assignments. We discovered that there are many ways to do this. After exploring various branches of mathematics, we settled on a simple formula (Rank= weight/time^2).

## Accomplishments that we're proud of

We are incredibly proud that we have a functional Back-End and that our UI is visually similar to our wireframes. We are also excited that we performed so well together as a newly formed group.

## What we learned

Keith used React for the first time. He learned a lot about responsive front-end development and managed to create a remarkable website despite encountering some issues with third-party software along the way. Gabriella designed the UI and helped code the front-end. She learned about input validation and designing features to meet functionality requirements. Eli coded the back-end using Flask and Python. He struggled with using Docker to deploy his script but managed to conquer the steep learning curve. He also learned how to use the Twilio API.

## What's next for StudyHedge

We are extremely excited to continue developing StudyHedge. As college students, we hope this idea can be as useful to others as it is to us. We want to scale this project eventually expand its reach to other universities. We'd also like to add more personal customization and calendar integration features. We are also considering implementing AI suggestions.