Applications,databases and open computer vision research from drone videos and images: a survey

Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an emerging application attracting significant attention from researchers in various areas of computer vision. Currently, the major challenge is the development of autonomous operations to complete missions and replace human operators. In this paper, based on the type of analyzing videos and images captured by drones in computer vision, we have reviewed these applications by categorizing them into three groups. The first group is related to remote sensing with challenges such as camera calibration, image matching, and aerial triangulation. The second group is related to drone-autonomous navigation, in which computer vision methods are designed to explore challenges such as flight control, visual localization and mapping, and target tracking and obstacle detection. The third group is dedicated to using images and videos captured by drones in various applications, such as surveillance, agriculture and forestry, animal detection, disaster detection, and face recognition. Since most of the computer vision methods related to the three categories have been designed for real-world conditions, providing real conditions based on drones is impossible. We aim to explore papers that provide a database for these purposes. In the first two groups, some survey papers presented are current. However, the surveys have not been aimed at exploring any databases. This paper presents a complete review of databases in the first two groups and works that used the databases to apply their methods. Vision-based intelligent applications and their databases are explored in the third group, and we discuss open problems and avenues for future research.

     

Gait recognition based on modified phase-only correlation

Gait recognition is an emerging biometric technology aiming to identify people purely through the analysis of the way they walk. The technology has attracted interest as a method of identification because it is noncontact and does not require the subject’s cooperation. Clothing, carrying conditions and other intra-class variations, also referred to as “covariates,” affect the performance of gait recognition systems. This paper proposes a supervised feature extraction method, which is able to select relevant discriminative features for human recognition to mitigate the impact of covariates and hence improve the recognition performances. The proposed method is evaluated using the CASIA gait database (dataset B), and the experimental results suggest that our method yields 81.40 % of correct classification when compared against similar techniques which do not exceed 77.96 %.     

Gait recognition for person re-identification

The Journal of Supercomputing - Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes....     

Micro-Mechanical Parameters in Short Fibre Composite

The aim of this paper is to analyse the contribution of micro-mechanical parameters, on the macroscopic behaviour of a short fibre reinforced thermoplastic composites (SFRTC). By developing an algorithm to provide a representative random micro-structure, a comparative analysis of different micro-mechanical parameters, such as aspect ratio (AR) and fibre orientation (FO), was conducted and compared with the existing analytical models. A study of different aspect ratios and different fibre orientations has been carried out in order to examine their effect on the linear elastic properties of SFRTC. Aspect ratios from one to ten have been analysed for the cases of fully oriented 0° fibres, miss-oriented fibres and randomly oriented fibres. A representative volume element (RVE) was used to investigate the effect of the representative size. Results were analysed statistically through X ² test, and the subsequent representative realisations were compared with the theoretical predictions.