A5011113664- Remote Sensing and LiDAR Applications
- 3D Surveying and Cultural Heritage
- Wood and Agarwood Research
- Remote Sensing in Agriculture
- Date Palm Research Studies
- Video Surveillance and Tracking Methods
- Advanced Vision and Imaging
- Horticultural and Viticultural Research
- Anomaly Detection Techniques and Applications
- COVID-19 diagnosis using AI
- Face recognition and analysis
- Face and Expression Recognition
- Forest Insect Ecology and Management
- Biometric Identification and Security
- Tree Root and Stability Studies
University of Alberta
2023-2024
National Institute of Technology Warangal
2020
Operational forest monitoring often requires fine-detail information in the form of an orthomosaic, created by stitching overlapping nadir images captured aerial platforms such as drones. RGB drone sensors are commonly used for low-cost, high-resolution imaging that is conducive to effective orthomosaicking, but only capture visible light. Thermal sensors, on other hand, long-wave infrared radiation, which useful early pest detection among applications. However, these lower-resolution suffer...
The practice of social distancing is imperative to curbing the spread contagious diseases and has been globally adopted as a non-pharmaceutical prevention measure during COVID-19 pandemic. This work proposes novel framework named SD-Measure for detecting from video footages. proposed leverages Mask R-CNN deep neural network detect people in frame. To consistently identify whether practiced interaction between people, centroid tracking algorithm utilised track subjects over course footage....
The use of facial masks in public spaces has become a social obligation since the wake COVID-19 global pandemic and identification can be imperative to ensure safety. Detection video footages is challenging task primarily due fact that themselves behave as occlusions face detection algorithms absence landmarks masked regions. In this work, we propose an approach for detecting videos using deep learning. proposed framework capitalizes on MTCNN model identify faces their corresponding present...
Bark beetle outbreaks can dramatically impact forest ecosystems and services around the world. For development of effective policies management plans, early detection infested trees is essential. Despite visual symptoms bark infestation, this task remains challenging, considering overlapping tree crowns non-homogeneity in crown foliage discolouration. In work, a deep learning based method proposed to effectively classify different stages attacks at individual level. The uses RetinaNet...
Accurate detection of individual tree crowns from remote sensing data poses a significant challenge due to the dense nature forest canopy and presence diverse environmental variations, e.g., overlapping canopies, occlusions, varying lighting conditions. Additionally, lack for training robust models adds another limitation in effectively studying complex This paper presents novel method detecting shadowed provides challenging dataset comprising roughly 50k paired RGB-thermal images facilitate...
Accurate detection of individual tree crowns from remote sensing data poses a significant challenge due to the dense nature forest canopy and presence diverse environmental variations, e.g., overlapping canopies, occlusions, varying lighting conditions. Additionally, lack for training robust models adds another limitation in effectively studying complex This paper presents novel method detecting shadowed provides challenging dataset comprising roughly 50k paired RGB-thermal images facilitate...