Over the past several years, significant progress has been made in object tracking, but challenges persist in tracking objects in high-resolution images captured from drones. Such images usually contain very tiny objects, and the movement of the drone causes rapid changes in the scene. In addition, the computing power of mission computers on drones is often insufficient to achieve real-time processing of deep learning-based object tracking. This paper presents a real-time on-drone pedestrian tracker that takes as the input 4K aerial images. The proposed tracker effectively hides the long latency required for deep learning-based detection (e.g., YOLO) by exploiting both the CPU and GPU equipped in the mission computer. We also propose techniques to minimize detection loss in drone-captured images, including a tracker-assisted confidence boosting and an ensemble for identity association. In our experiments, using real-world inputs captured by drones at a height of 50 m, the proposed method with an NVIDIA Jetson TX2 proves its efficacy by achieving real-time detection and tracking in 4K video streams.


    Access

    Download


    Export, share and cite



    Title :

    Towards Real-Time On-Drone Pedestrian Tracking in 4K Inputs


    Contributors:
    Chanyoung Oh (author) / Moonsoo Lee (author) / Chaedeok Lim (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Real-Time Pedestrian Tracking and Counting with TLD

    Jiawei Shi / Xianmei Wang / Huer Xiao | DOAJ | 2018

    Free access

    Towards Real-Time Drone Detection Using Deep Neural Networks

    Pulido, Cristhiam / Ceron, Alexander | Springer Verlag | 2021


    SYSTEM AND METHOD FOR CONTROLLING DRONE MOVEMENT FOR OBJECT TRACKING USING ESTIMATED RELATIVE DISTANCES AND DRONE SENSOR INPUTS

    PIERCE BRIAN / ENGLISH ELLIOT / KUMAR ANKIT et al. | European Patent Office | 2017

    Free access

    Real-Time Pedestrian Detection and Tracking Based on YOLOv3

    Li, Xingyu / Hu, Jianming / Liu, Hantao et al. | TIBKAT | 2022


    Real-Time Pedestrian Detection and Tracking Based on YOLOv3

    Li, Xingyu / Hu, Jianming / Liu, Hantao et al. | ASCE | 2022