Gate volume is an integral part of many algorithms for non-parametric target tracking in clutter. Non-parametric target tracking assumes no a-priori knowledge of clutter measurement density. In the simplest case of a single-target single-state target tracking filter employing a Gaussian approximation, the gate is a hyper-ellipsoid whose volume can be calculated analytically. However, in a multi...

Title of Thesis: Multi-target Detection, Tracking, and Data Association on Road Networks Using Unmanned Aerial Vehicles Brett E. Barkley, Master of Science, 2017 Thesis directed by: Professor Derek A. Paley Department of Aerospace Engineering A cooperative detection and tracking algorithm for multiple targets constrained to a road network is presented for fixed-wing Unmanned Air Vehicles (UAVs)...

Target tracking has been a research hotspot in computer vision, and the correlation filtered target algorithm benefits of low computational complexity fast speed. Still, effect is not good when dealing with complicated circumstances. This paper proposes multi-feature fusion repositioning for problem complex environments. First, weighted presented. Since each feature different advantages environ...

Robust moving target tracking has become an important topic in the field of computer vision. The fusion of feature such as color, texture, edge strength and motion has proved to be a promising approach to robust visual tracking in situations where no single feature is suitable. Due to the poor tracking performance in complicated scenarios, a new visual target tracking scheme is proposed. This a...

Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and secondary surveillance radar as the active sensor on board. The electronics and communication support measure system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect, identify, classify the different threats present in the surveillanc...

In this paper, we propose the object tracking method based on color histograms and particle filtering. Particle filtering is a time series filter for estimating a state using probabilistic approach. Unlike deterministic approach such as template matching algorithm, it is more robust to occlusion or clutter because of its having many hypotheses. Moreover, color histograms are robust to partial o...