Human detection in MOUT scenarios using covariance descriptors and supervised manifold learning

Military Operations in Urban Terrain (MOUT) require the capability to perceive and to analyse the situation around a patrol in order to recognize potential threats. As in MOUT scenarios threats usually arise from humans one important task is the robust detection of humans. Detection of humans in MOUT by image processing systems can be very challenging, e.g., due to complex outdoor scenes where humans have a weak contrast against the background or are partially occluded. Porikli et al. introduced covariance descriptors and showed their usefulness for human detection in complex scenes. However, these descriptors do not lie on a vector space and so well-known machine learning techniques need to be adapted to train covariance descriptor classifiers. We present a novel approach based on manifold learning that simplifies the classification of covariance descriptors. In this paper, we apply this approach for detecting humans. We describe our human detection method and evaluate the detector on benchmark data sets generated from real-world image sequences captured during MOUT exercises.

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