Season 1 Challenges

OPENED ON: 09 NOV 2017  |  CLOSED ON: 09 FEB 2018  |  REWARD: INR 3,00,000
Reward money is paid in exchange of legally acquiring the solution, implementing it to solve the problem and meeting the success criteria. Milestones for paying the reward money would depend upon the complexity of challenge and maturity of the proposed solution, which would be discussed with the solver as soon as the proposed solution is selected by us.
Rewarded

Short Description:

Drones are used to capture images and these images are GPS tagged. To get a complete picture of the area these images are to be merged together. Solutions are invited to seamlessly stitch the images.

Challenge Details

Drones are used to capture images of factory premise for surveillance and record keeping purposes. To capture the images the Drone is flown over the area of interest at an elevation of about 30 to 40 meters. The area is divided into several sectors depending on size of the area. Typically, the sector is divided into 8 to 12 sectors. The Drone flies to the specified location to capture the images. The location in a particular sector, where the image is to be captured and the path to be traversed is determined by a program for autonomous flights. The images can be captured using manual mode as well.  All the images captured by Drone are tagged a Geo coordinates along with elevation and inclination data of the point at which the image is captured.  Each image is of 4 – 5 MB in size. They are stored in JPEG format.

This way a lot of Geo referenced images are captured wherein each picture captures only a part of the whole area. To get the image of the entire area these segregated images must be stitched together.  While stitching it is to be made sure proper images are put next to each other depending on their Geo reference coordinates.

We are looking for solutions that will enable us to stitch such images (that we have) to get a holistic 2D images of the area.  

Success Criteria

  1. Should be able to handle various images formats to produce one holistic 2D image
  2. Should not be resource intensive. It should run on commercially available laptops
  3. It will be an added advantage if it can also produce a 3D reconstruction of the area of interest using the same images

 

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