Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle

Abstract

This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences

“…However, infrared images have relatively low contrast and signal-to-noise ratios (SNR) and also contain little target information, and so the detection and tracking of moving targets using infrared imagery is difficult. In addition, the use of moving imaging platforms such as aircraft gives rise to the problems of background motion and low target resolution [1,2], and correspondingly raises the requirements for the detection and tracking technology that is used.…”

Airborne Downward Looking Sparse Linear Array 3-D SAR Heterogeneous Parallel Simulation

“…A well reconstructed 3-D image can be developed for city planning [43,44], environmental monitoring [45], Digital Surface Model (DSM) generation [46], disaster relief [47], surveillance and reconnaissance [48], etc.…”

Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface Differentiation

“…This is mainly due to the need to apply the newest, least time-consuming technologies to analyze large areas to maintain safety standards and to provide knowledge on road age and distress. Recent studies [6][7][8][9] show a growing interest in remote and proximal sensing applications for road network monitoring and management.…”