Design and evaluation of a hierarchy of boosted classifiers for detection of ground targets in aerial surveillance imagery

摘要

One way to increase the robustness and efficiency of unmanned surveillance platforms is to introduce an autonomous data acquisition capability. In order to mimic a sensor operator's search pattern, combining wide area search with detailed study of detected regions of interest, the system must be able to produce target indications in real time. Rapid detection algorithms are also useful for cueing image analysts that process large amounts of aerial reconnaissance imagery. Recently, the use of a sequence of increasingly complex classifiers has by several authors been suggested as a means to achieve high processing rates at low false alarm and miss rates. The basic principle is that much of the background can be rejected by a simple classifier before more complex classifiers are applied to analyse more difficult remaining image regions. Even higher performance can be achieved if each detector stage is implemented as a set of expert classifiers, each specialised to a subset of the target training set. In order to cope with the increasingly difficult classification problem faced at successive stages, the partitioning of the target training set must be made increasingly fine-grained, resulting in a coarse-to-fine hierarchy of detectors. Most of the literature on this type of detectors is concerned with face detection. The present paper describes a system designed for detection of military ground vehicles in thermal imagery from airborne platforms. The classifier components used are trained using a variant of the LogitBoost algorithm. The results obtained are encouraging, and suggest that it is possible to achieve very low false alarm and miss rates for this very demanding application.