Obstacle Detection in Drones Using Computer Vision Algorithm

  • N. AswiniEmail author
  • Satyanarayana Visweswaraiya Uma
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)


Obstacle detection and collision avoidance are complicated particularly in drones where accuracy matters a lot to avoid collision between a vehicle and an object. This complication arises due to restricted number of heavy sensors like radar. To overcome the drawbacks of heavy sensor, light weight monocular cameras can be employed. Monocular cameras are capable of analyzing and computing depth by giving the three-dimensional representation. In the proposed method, key point features are extracted from each video frame using Computer vision algorithms like Harris corner detector and Scale Invariant Feature Transform algorithm (SIFT). Then by using Brute Force Matching (BFM), key points of consecutive frames are matched. As drone move towards obstacle, size of obstacle increases i.e. convex hull size around key point increases which shows obstacle is detected.


Unmanned Aerial Vehicle (UAV) Harris corner detector Scale Invariant Feature Transform Brute Force Matching Obstacle detection 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.MVJ College of EngineeringVisveswaraya Technological UniversityBangaloreIndia
  2. 2.RNS Institute of TechnologyVisveswaraya Technological UniversityBangaloreIndia

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