Deep background subtraction of thermal and visible imagery for redestrian detection in videos

In this paper, we introduce an efficient framework to subtract the background from both visible and thermal imagery for pedestrians’ detection in the urban scene. We use a deep neural network (DNN) to train the background subtraction model. For the training of the DNN, we first generate an initial background map and then employ randomly 5% video frames, background map, and manually segmented ground truth. Then we apply a cognition-based post-processing to further smooth the foreground detection result. We evaluate our method against our previous work and 11 recently widely cited method on three challenge video series selected from a publicly available color-thermal benchmark dataset OCTBVS. Promising results have been shown that the proposed DNN-based approach can successfully detect the pedestrians with good shape in most scenes regardless of illuminate changes and occlusion problem

Machine-learning nonstationary noise out of gravitational-wave detectors

Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation

A fully-convolutional neural network for background subtraction of unseen videos

Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature. In this work, we propose a new, supervised, backgroundsubtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.Accepted manuscrip