Abstract:
Human target localization has a plethora of ubiquitous applications, including healthcare and security. Reliable location estimation is challenging in outdoor settings because most targets do not carry, wear, or attach a sensor to their bodies to provide motion information. Seismic and audio sensors can be used extensively to monitor targets in an unobtrusive manner. This paper proposes regression-based methods to localize a human target in an outdoor environment using seismic and audio signatures. The approaches fall into two groups. The first group of approaches applies a late fusion on the audio and seismic modality for target localization. First, a regression algorithm on the seismic signals is applied to estimate the distance of the target from each sensor; then, the target distance information is combined with the audio direction to infer the target location. In contrast to the first group of approaches, the second group of approaches applies an early fusion of audio and seismic modality to directly predict the target location. We compare the proposed methods with multiple state-of-the-arts on different evaluation measures. On 5-fold cross-validation, we achieve a root-mean-localization error of 0.735 and 0.907 meters in an area of 324 meter2, for dataset-1 and dataset-2, respectively. The best results with proposed approaches show an improvement of 4.68 and 4.57 meters over the best performing state-of-the-art approach for dataset-1 and dataset-2, respectively. We also analyzed the generalization capabilities of the proposed methods. Further, a detailed ablation study on the number of sensors required for localization, feature selection, training data requirement, etc., has been carried out.
