  We propose a new order preserving bilinear framework that exploits
low-resolution video for person detection in a multi-modal setting using deep
neural networks. In this setting cameras are strategically placed such that
less robust sensors, e.g. geophones that monitor seismic activity, are located
within the field of views (FOVs) of cameras. The primary challenge is being
able to leverage sufficient information from videos where there are less than
40 pixels on targets, while also taking advantage of less discriminative
information from other modalities, e.g. seismic. Unlike state-of-the-art
methods, our bilinear framework retains spatio-temporal order when computing
the vector outer products between pairs of features. Despite the high
dimensionality of these outer products, we demonstrate that our order
preserving bilinear framework yields better performance than recent orderless
bilinear models and alternative fusion methods.
