Drone-surveillance for search and rescue in natural disaster

• Published in: Computer communications Vol. 156; pp. 1 - 10
• Main Authors: Mishra, Balmukund, Garg, Deepak, Narang, Pratik, Mishra, Vipul
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.04.2020
• Subjects: Action recognition; Aerial action dataset; Convolution neural network (CNN); Drone surveillance; Object detection (OD); Action recognition; Object detection (OD); Convolution neural network (CNN); Aerial action dataset; Drone surveillance
• ISSN: 0140-3664; 1873-703X
• DOI: 10.1016/j.comcom.2020.03.012

Due to the increasing capability of drones and requirements to monitor remote areas, drone surveillance is becoming popular. In case of natural disaster, it can scan the wide affected-area quickly and make the search and rescue (SAR) faster to save more human lives. However, using autonomous drone for search and rescue is least explored and require attention of researchers to develop efficient algorithms in autonomous drone surveillance. To develop an automated application using recent advancement of deep-learning, dataset is the key. For this, a substantial amount of human detection and action detection dataset is required to train the deep-learning models. As dataset of drone surveillance in SAR is not available in literature, this paper proposes an image dataset for human action detection for SAR. Proposed dataset contains 2000 unique images filtered from 75,000 images. It contains 30000 human instances of different actions. Also, in this paper various experiments are conducted with proposed dataset, publicly available dataset, and stat-of-the art detection method. Our experiments shows that existing models are not adequate for critical applications such as SAR, and that motivates us to propose a model which is inspired by the pyramidal feature extraction of SSD for human detection and action recognition Proposed model achieves 0.98mAP when applied on proposed dataset which is a significant contribution. In addition, proposed model achieve 7% higher mAP value when applied to standard Okutama dataset in comparison with the state-of-the-art detection models in literature.