Pitch: Crowdsourced AI Security


Crime has substantial societal costs: local government resources, medical bills, increased spending by companies and individuals on security, and depressed property values, to name a few. The global security market was valued at $70.02 billion in 2016 and is projected to grow substantially in the next eight years. Rising terrorism and mass shooting concerns in the US and abroad has led to a surge in the adoption of security systems. Additionally, technological proliferation, smart city infrastructure, and advanced analytics are transforming the industry.

The market is sizeable arguably because people have high willingness to pay to protect their loved ones and their property. Robberies, shootings, kidnappings, and even missing pets generate high levels of physical and emotional stress and significant costs for the victims and their communities. Effective use of analytics for crime detection and prevention is thus the key to saving and improving lives.


Solution and Feasibility:

Our proposal is to tackle security challenges by aggregating data from public and private cameras and sourcing image, video, and voice recordings from individuals. Currently people witnessing a crime are often willing to make recordings but there is no centralized platform for affected communities that can aggregate the incoming data. This causes delays in law enforcement reaction and some evidence likely never gets reported. A centralized crowdsourced data platform augmented with data from private surveillance cameras and public cameras such as ATM and supermarket surveillance can be effective in crisis situations such as location a runaway robber, terrorist, or missing people or pets. At later stages, the company will be able to identify high risk areas with limited coverage and augment the system capabilities by installing cameras or sound sensors there. Local governments or businesses interested in the area can subsidize the cost of the new sensors.

Further capabilities for crime prevention that the company will develop is to use AI and machine learning to identify faces of criminal suspects in the data streams. Machine learning can be used to set alerts based on detecting things like people with face masks, firearms, or unattended bags in crowded areas. Similarly, machine learning can be used to scan the data for the faces of missing people or images of runaway pets.

The number of security cameras in North America effectively doubled between 2014 and 2016. Along with an increase in mobile devices, the proliferation of drones and satellites will provide additional data for the platform.



Crime detection and prevention solutions will be of interest to local authorities. We plan to pilot the technology by partnering with local authorities who are interested in using AI to help improve security. For example, in New Orleans, Mayor Mitch Landrieu has proposed a $40 million crime-fighting surveillance plan, which will combine municipal cameras with the live feeds from private webcams operated by businesses and individuals. As such, we plan to team up with New Orleans and other cities to demonstrate our capabilities. Other areas for expansion include airports, crowded public spaces, and college campuses.



There are several companies which are using AI to help detect and predict crime. ShotSpotter, for instance, already uses sensor data to detect and alert authorities in the case of gun fire. ShotSpotter can provide information on the type of weapon and the likely location where it was fired with accuracy within 10 feet. The company had an IPO in 2017 and claims to have presence in over 90 cities in the US. Hikvision, a Chinese security camera producer, uses facial recognition to search for criminals or detect suspicious activity such as unattended bags. This technology is being used in the US in varying situations, such as by the Memphis police (for crime) or by the U.S. Army (to monitor a base). Many of the competitors are focused more on sensors and security cameras, which will have more power and impact if they can be utilized along with image, video, and voice recording from individuals. As such, our centralized crowdsourced data platform will provide a richer and more holistic view of the data. Further, many of our competitors are very specialized and niche. Over time, we plan to expand our capabilities to focus not just on crime but also on good Samaritan acts such as finding missing items or returning lost pets.



In the wrong hands, the technology can be used for the opposite purpose and actually aid crime. Criminals can identify when people leave their homes on vacation or target individuals. This will be mitigated by building in special access rights into the platform so that the general public can upload images and videos but special groups such as law enforcement officers can utilize the data.

Privacy concerns also have to be addressed. These concerns will be mitigated by ensuring transparency about the data sources and using data sources that the police can already get access to. Despite data privacy concerns, in crisis situations people are often very willing to assist authorities as much as they can to speed up the recovery of their loved ones.




Team Members:

Sam Steiny

Rosie Newman

Gergana Kostadinova

Javier Rodriguez

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