Selections of suitable UAV imagery’s configurations for regions classification

Abstract

Unmanned Aerial Vehicles (UAV) are used to conduct a variety of recognition as well as specific missions such as target tracking, safe landing. The transmitted image sequences to be interpreted at ground station usually face limited requirements of the data transmission. In this paper, on one hand, we handle a surveillance mission with segmenting a UAV video’s content into semantic regions. We deploy a spatio-temporal framework that considers UAV videos specific characteristics for segmenting multi regions of interest. After post-processing steps on the segmentation results, a support vector machine classifier is used to recognize regions. In term of temporal feature, we combine the results from the previous frames by proposing to use a state transition formulating through a Markov model. On the other hand, this study also assesses the influences of data reduction techniques on the proposed techniques. The comparisons between the untreated configuration and control conditions under manipulations of the frame rate, spatial resolution, and compression ratio, demonstrate how these data reduction techniques adversely influence the algorithm’s performance. The experiments also point out the optimal configuration in order to obtain a trade-off between the target performance and limitation of the data transmission.