2 research outputs found
Unmanned aerial vehicle video-based target tracking algorithm Using sparse representation
Target tracking based on unmanned aerial vehicle
(UAV) video is a significant technique in intelligent urban
surveillance systems for smart city applications, such as smart
transportation, road traffic monitoring, inspection of stolen
vehicle, etc. In this paper, a vision-based target tracking algorithm
aiming at locating UAV-captured targets, like pedestrian and
vehicle, is proposed using sparse representation theory. First of all,
each target candidate is sparsely represented in the subspace
spanned by a joint dictionary. Then, the sparse representation
coefficient is further constrained by an L2 regularization based on
the temporal consistency. To cope with the partial occlusion
appearing in UAV videos, a Markov Random Field (MRF)-based
binary support vector with contiguous occlusion constraint is
introduced to our sparse representation model. For long-term
tracking, the particle filter framework along with a dynamic
template update scheme is designed. Both qualitative and
quantitative experiments implemented on visible (Vis) and
infrared (IR) UAV videos prove that the presented tracker can
achieve better performances in terms of precision rate and success
rate when compared with other state-of-the-art tracker