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Collaborative Drone Footage Capture Utilizing Predictive Analytics Disclosure Number: IPCOM000246021D
Publication Date: 2016-Apr-26
Document File: 6 page(s) / 119K

Publishing Venue

The Prior Art Database


Disclosed are a system and method that create a collaborative network of privately owned consumer drones that can be utilized to coordinate the capture of photographic and video footage of an event. In addition to predicting the availability of operational drones in an area at any given time, the system predicts near term future events that can benefit from drone footage, intelligently selects drones to capture the footage, and stores the resulting content from the drones in a central media library.

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Collaborative Drone Footage Capture Utilizing Predictive Analytics

Unmanned aerial vehicles, otherwise known as drones, are becoming increasing popular. Most consumer drones operate on a local network. A typical example is shown in Figure 1. The drone creates a local network over Wi-Fi. Devices connect to this network (often using a Wi-Fi extender to boost range) to send and receive signals to the drone. For example, an application (app) running on a mobile device can receive real time location information from the drone and live streaming video from the drone's onboard camera. A remote control unit uses this network to send control signals to maneuver the drone.

Figure 1: Typical operational model of a drone, using a local network to send and receive information to the drone

While this local network setup provides a good method to control a single drone, it does not describe a method to coordinate a series of drones to respond to a given situation. Specifically, there is no defined method to coordinate the use of the cameras on board consumer drones, regardless of drone manufacturer, within a given area to capture photographs and videos of an event.

For example, an incident occurs that generates a need or desire for aerial photography or videography. This may be a scheduled event (e.g., a parade is passing through town), unscheduled (e.g., a major crash on a freeway), or predictable (e.g., potential traffic build-up). Several privately owned consumer drones are near the incident and have the potential to capture the required aerial photography if 1) the events are


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anticipated, 2) drones are alerted to the incident, 3) drones are coordinated to avoid collisions and provide a variety of footage, and 4) the footage can be collectively combined at a central location.

The novel contribution is a method and system to collectively utilize the camera capabilities of consumer and professional drones in response to events. A core component is an event notification system central repository that stores the capabilities and current location of privately owned drones for the purpose of media capture. Nearby registered drones are identified, analyzed, selected, and notified to capture photographic and video footage for a given event. The footage from each drone is collated to form a central media library of the event.

In addition, the solution includes an anticipated event prediction system that allows a user to predict an upcoming event suitable for drone footage, based on a set of criteria and defined data sources. A novel prediction engine , based upon historical data, determines the likelihood of privately owned drones flying in a given area, and the likelihood of drones accepting requests to capture photographic and video footage of an event at a given location on a given date and time.

Figure 2: Core components

Implementation of the method and system for collaborative drone footage capture utilizing predictive analytics c...