2017/12/20 by Oytun Ulutan, Benjamin S. Riggan, Ulutan, Oytun +6
Computer Science · #Advanced Neural Network Applications #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1712.07721
openalex publication_date 2017/12/20 · arxiv created 2018/01/11 · arxiv updated 2018/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.