Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment
<|reference_start|>Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment: In this paper, we have proposed a new strategy of using the landmark anchor node instead of a radio-based anchor node to obtain the virtual coordinates (landmarkID, DISTANCE) of moving troops or defense forces that will help in tracking and maneuvering the troops along a safe path within a GPS-denied battlefield environment. The proposed strategy implements landmark recognition using the Yolov5 model and landmark distance estimation using an efficient Stereo Matching Algorithm. We consider that a moving node carrying a low-power mobile device facilitated with a calibrated stereo vision camera that captures stereo images of a scene containing landmarks within the battlefield region whose locations are stored in an offline server residing within the device itself. We created a custom landmark image dataset called MSTLandmarkv1 with 34 landmark classes and another landmark stereo dataset of those 34 landmark instances called MSTLandmarkStereov1. We trained the YOLOv5 model with MSTLandmarkv1 dataset and achieved 0.95 mAP @ 0.5 IoU and 0.767 mAP @ [0.5: 0.95] IoU. We calculated the distance from a node to the landmark utilizing the bounding box coordinates and the depth map generated by the improved SGM algorithm using MSTLandmarkStereov1. The tuple of landmark IDs obtained from the detection result and the distances calculated by the SGM algorithm are stored as the virtual coordinates of a node. In future work, we will use these virtual coordinates to obtain the location of a node using an efficient trilateration algorithm and optimize the node position using the appropriate optimization method.<|reference_end|>
@article{sapkota2024landmark,
    title={Landmark Stereo Dataset for Landmark Recognition and Moving Node
  Localization in a Non-GPS Battlefield Environment},
    author={Ganesh Sapkota, Sanjay Madria},
    journal={2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), St.
  Louis, MO, USA, 2023, pp. 1-11},
    year={2024},
    doi={10.1109/AIPR60534.2023.10440690},
    archivePrefix={arXiv},
    eprint={2402.12320},
    primaryClass={cs.CV cs.LG}
}