Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/1143
Title: Intelligent annotation tool for multi-sensor visual recognition tasks
Authors: Deshwal, Aruj
Rawat, Abhinav
Anand, Saket (Advisor)
Shukla, Jainendra (Advisor)
Shah, Rajiv Ratn (Advisor)
Keywords: 3D Bat
ROS
KITTI
Lidar segmentation
Issue Date: Dec-2022
Publisher: IIIT-Delhi
Abstract: This project aims to create a multi sensor annotation tool capable of annotating lidar and camera data. To reduce annotation time it will have the capability of creating annotations for the camera images by projecting the annotations from the 3D space into the image domain. The tool will possess semi automatic labelling features through the integration of object detection models such as Yolo. The tool will have the capability to select images using Active learning strategies to reduce the annotation effort and cost for fine-tuning the object detection model. Interpolation and tracking features will also be present which will track an annotated object across frames and different cameras and this will further reduce the time required for the annotations. The tool will also possess lidar segmentation models to label the lidar data. It will be able to read data provided by multiple sensors and process annotations to give calibration parameters for lidar as an output. Existing tools do not possess all of these features. This would be a novel tool especially for Indian data.
URI: http://repository.iiitd.edu.in/xmlui/handle/123456789/1143
Appears in Collections:Year-2022

Files in This Item:
File Description SizeFormat 
Aruj Deshwal.pdf
  Restricted Access
2.44 MBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.