Search

The capability of the unmanned aerial vehicle (UAV) to capture highly informative data has expanded its utility in multiple sectors. Surveillance-based UAV applications have highly relied on accurate object tracking. During UAV monitoring, issues such as changes in object appearance and occlusion are common. Tracking the objects under such a scenario is a challenging task. On the other hand, accurate object tracking is quintessential in critical scenarios like security surveillance. In this work, a novel deep learning-based framework for accurate multiple objects tracking with UAV videos is proposed. Tiny-Deeply Supervised Object Detector (Tiny-DSOD) is adopted for accurate object detection. A novel stacked bidirectional-forward LSTM (SBF-LSTM) tracker with spatial and visual features is proposed for object tracking. The spatial and visual features obtained from Tiny-DSOD are trained with the tracker, which predicts object location during tracking. The choice of SBF-LSTM as the tracker enables accurate prediction of object location. Object association is dealt with based on bounding box distance, appearance, and size metrics. With the proposed model, switching object identities is lessened to a greater extent, thereby increasing the tracking accuracy. The proposed methodology outperforms the state-of-the-art methods on UAV videos. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]