Deep Learning Based Human Detection and Tracking Drone
Over the past few years, the use of drones has rapidly grown in popularity offering multipurpose uses, the drones have become an important focus in various applications including agricultural monitoring, disaster management, surveillance, remote sensing, target acquisition, border patrol, infrastruc
2025-06-28 16:31:05 - Adil Khan
Deep Learning Based Human Detection and Tracking Drone
Project Area of Specialization Artificial IntelligenceProject SummaryOver the past few years, the use of drones has rapidly grown in popularity offering multipurpose uses, the drones have become an important focus in various applications including agricultural monitoring, disaster management, surveillance, remote sensing, target acquisition, border patrol, infrastructure monitoring, photography, and videography. Moreover, drones are a useful tool for researchers to test and evaluate new ideas in various fields, including flight controls theory, navigation, real-time systems, and robotics.
In this project, a deep learning-based Real-time Human detection and tracking are proposed. In the first phase, a custom deep learning framework will be designed and trained using the Stanford Drone dataset.
In the second phase, advanced computer vision technology shall be employed to track the particular human.
Initially, the proposed techniques will be validated using simulations and then real-time flights on a quadcopter will be carried out.
And this project will be really very helpful for all our security institutions like the police and intelligence agencies in identifying and tracking a suspicious person. And it will also help in rescue operations in difficult areas like forests etc.
Project Objectives1) Develop a deep learning-based human detection algorithm using Pytorch.
2) Develop a deep learning-based human tracking algorithm using Pytorch.
3) Setup the hardware, mount the gimbal and camera to the drone
4) Implementation of the developed algorithms on Drone for real-time test flights.
Project Implementation MethodHardware
- Purchasing of hardware components based on design requirements
- Setting Up the Hardware components and mounting to the drone
- Integration of camera for live video streaming
- Transmission of a video stream to display via video transceiver/router
Software
- Deep Learning-based human detection using YOLO/Retinanet framework
- Feature-based tracking using real-time tracker DEEPSORT
- Training of the network using Stanford Drone Dataset
- Testing the simulations and improving the network parameters for desired results
Hardware and software integration
- Implement the developed network architecture on the Drone's Jetson Board
It can be used in the detection of humans in a given area and tracking a particular selected human. Law enforcing agencies can use for aerial surveillance of particular areas at the borders in case of an enemy or an intruder. Vloggers can use this for aerial video shots including the tracking feature.
Technical Details of Final DeliverableA machine learning/ deep learning-based human detection & tracking algorithms will be designed and implemented on a Jetson Tx1 and real-time test flights of quadcopter will be conducted.
Final Deliverable of the Project HW/SW integrated systemCore Industry SecurityOther Industries Others Core Technology Artificial Intelligence(AI)Other Technologies RoboticsSustainable Development Goals Industry, Innovation and InfrastructureRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 66000 | |||
| RC Drone Camera Gimbal, Metal Brushless storm 32 Gimbal Board | Equipment | 2 | 20000 | 40000 |
| 8MP Raspberry pi camera | Equipment | 1 | 10000 | 10000 |
| Clip on lens for zoom | Equipment | 1 | 10000 | 10000 |
| Additional battery for quadcopter | Miscellaneous | 1 | 6000 | 6000 |