Convolutional neural network-based real-time object detection and tracking for parrot AR drone 2.

Rohan, Ali; Rabah, Mohammed; Kim, Sung-Ho

Abstract

Recent advancements in the field of Artificial Intelligence (AI) have provided an opportunity to create autonomous devices, robots, and machines characterized particularly with the ability to make decisions and perform tasks without human mediation. One of these devices, Unmanned Aerial Vehicles (UAVs) or drones are widely used to perform tasks like surveillance, search and rescue, object detection and target tracking, parcel delivery (recently started by Amazon), and many more. The sensitivity in performing said tasks demands that drones must be efficient and reliable. For this, in this paper, an approach to detect and track the target object, moving or still, for a drone is presented. The Parrot AR Drone 2 is used for this application. Convolutional Neural Network (CNN) is used for object detection and target tracking. The object detection results show that CNN detects and classifies object with a high level of accuracy (98%). For real-time tracking, the tracking algorithm responds faster than conventionally used approaches, efficiently tracking the detected object without losing it from sight. The calculations based on several iterations exhibit that the efficiency achieved for target tracking is 96.5%.

Citation

ROHAN, A., RABAH, M. and KIM, S.-H. 2019. Convolutional neural network-based real-time object detection and tracking for parrot AR drone 2. IEEE access [online], 7, pages 69575-69584. Available from: https://doi.org/10.1109/ACCESS.2019.2919332

• Journal Article Type: Article
• Acceptance Date: May 18, 2019
• Online Publication Date: May 27, 2019
• Publication Date: Dec 31, 2019
• Deposit Date: Jul 18, 2023
• Publicly Available Date: Jul 18, 2023
• Journal: IEEE access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 69575-69584
• DOI: https://doi.org/10.1109/ACCESS.2019.2919332
• Keywords: Convolutional neural network; Deep learning; Object detection; Target tracking; Unmanned aerial vehicles
• Public URL: https://rgu-repository.worktribe.com/output/1982303