| Title |
Single Object Tracking in Plenoptic Sequences via Similarity Estimation |
| DOI |
https://doi.org/10.5573/ieie.2021.58.2.33 |
| Keywords |
Object tracking; plenoptic sequence; similarity estimation; search region restriction |
| Abstract |
Single object tracking is one of the conventional fields in computer vision, and which is being employed by various applications including surveillance, defense, and autonomous driving. Recent 2D object tracking techniques adopt a similarity estimation between the extracted features from a target object and search regions by feeding them into a Siamese network. Such deep learning based object tracking methods have led the improved performances regarding both robustness and real-time capability, however, tracking the partially or even fully occluded object is challenging, and which is still remained as an insurmountable technical huddle in the related field. In order to resolve this problem, we introduce the novel plenoptic object tracking method guaranteeing the reliable performance when occlusion occurs. A focal stack can be rendered by plenoptic imaging with the calibrated multiple cameras, and which consists of several focal planes representing different focus regions. Differ to general 2D sequences, some of focal planes in a focal stack provide weak appearance information to track the target object stemmed from disparities of the separately located cameras. Thus, we utilize such characteristics to track an object in plenoptic sequences by estimating similarity between the features of target object and focal planes in hyperspace which are captured by a weight-sharing structured network. Additionally, towards minimizing the plenoptic object tracking error mainly caused by an exhaustive search over all focal planes, the adaptive search region restriction algorithm is also proposed. Through applying the proposed plenoptic object tracking scheme, the results show that promising performance can be achieved when even a target object is invisible. |