From 7ed2096a9e3a8923f87dbac4b4c8840b59447b7a Mon Sep 17 00:00:00 2001 From: batuhanaavci Date: Sun, 10 Nov 2024 00:10:59 +0200 Subject: [PATCH] minor change --- _portfolio/portfolio-1.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/_portfolio/portfolio-1.md b/_portfolio/portfolio-1.md index 7928931576c4a..2461ff3c4926d 100644 --- a/_portfolio/portfolio-1.md +++ b/_portfolio/portfolio-1.md @@ -21,3 +21,23 @@ Below is a video demonstrating the implementation of control methods on the Quan ### Control Implementation ![PID Control on Servo 2](/images/servo2_pid.jpeg) ![LQR Control Balancing](/images/servo2_lqr.jpeg) --> + + +--- +title: "Recognizing and Tracking Person of Interest: A Real-Time Efficient Deep Learning based Method for Quadcopters" +excerpt: "Participated in MATLAB and Simulink Challenge Projects program. Extended to a conference paper." +
" +collection: portfolio +--- + +The recognition and tracking of a person of interest is a crucial task in many applications, including search and rescue, security, and surveillance. This paper presents a distributed system architecture that leverages the asynchronous threading and communication property of ROS2 to develop and implement a real-time efficient Deep Learning (DL) based method for recognizing and tracking a person of interest. The DL model receives snapshots from the quadcopter's camera and sends back an information vector, which includes all recognized persons and their corresponding position information within the camera frame of the quadcopter. The person of interest tracking control system receives face set information about the person of interest and generates reference velocity signals to be tracked by low-level controllers embedded within the drone. Experiments conducted in a cluttered and complex environment demonstrate the efficiency of the DL-based architecture for quadcopters. The presented real- world results validate the effectiveness of the proposed approach in recognizing and tracking a person of interest. + + +## Video Demonstration + +Below is a video demonstrating our work: + +[![Real-time Demo](https://img.youtube.com/vi/i7bYXnRy8Vc/0.jpg)](https://www.youtube.com/watch?v=i7bYXnRy8Vc) + +## Images +