Detection of targets in forward-looking infrared imaging using a multiple instance learning framework

Abstract

In this paper we describe a method for generating cues of targets present in the field-of-view of an infrared (IR) camera installed on a moving vehicle. A typical two class classifier requires the building of an image library containing manually extracted examples of both types of objects, i.e., targets and non-targets. This approach, usually tedious on static images, becomes intractable when using video sequences taken from a moving vehicle. To avoid a detailed manual segmentation of video sequences, we employ a multiple instance learning (MIL) framework that allows training a two class classifier just by specifying if a frame contains targets or not. The proposed method has three steps. First, for each frame of a training run, we generate a set of possible points of interest using a corner detection algorithm. Second, for the same training run, we tag each frame as positive (target hits present) or negative (only non-target hits present). Each hit is described using the local binary pattern (LPB) features computed around its image location. The generated LPB feature vectors, together with their frame tag, are used by the MIL training framework to generate a set of target LPB prototypes. Although many regular classifiers may be trained using MIL, in this paper we employed a simple approach based on the nearest prototype. In the last step, we used the computed prototypes to classify the corner hits detected in several test video sequences. To validate our approach, we present results obtained on several runs gathered with a long wave infrared (LWIR) camera mounted on a moving vehicle.