UAVs have been deployed in various object tracking applications such as disaster management, traffic monitoring, wildlife monitoring and crowd management. Recently, various deep learning methodologies have a profound effect on object detection and tracking. Deep learning-based object detectors rely on pre-trained networks. Problems arise when there is a mismatch between the pre-trained network domain and the target domain. UAV images possess different characteristics than images used in pre-trained networks due to camera view variation, altitude ranges and camera motion. In this paper, we propose a novel methodology to detect and track objects from UAV data. A deeply supervised object detector (DSOD) is entirely trained on UAV images. Deep supervision and dense layer-wise connection enriches the learning of DSOD and performs better object detection than pre-trained-based detectors. Long–Short-Term Memory (LSTM) is used for tracking the detected object. LSTM remembers the inputs from the past and predicts the object in the next frame thereby bridging the gap of undetected objects which improves tracking. The proposed methodology is compared with pre-trained-based models and it outperforms.
All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Earth and Planetary Sciences (miscellaneous)