This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.

Background subtraction is a crucial step in many automatic video content analysis applications. While numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of adaptability to multiple environments, noise resilience, and computation efficiency. In this paper, we present a powerful method for background extraction that improves in accuracy and reduces the computational load. The main innovation concerns the use of a random policy to select values to build a samples-based estimation of the background. To our knowledge, it is the first time that a random aggregation is used in the field of background extraction. In addition we propose a novel policy that propagates information between neighboring pixels of an image. Experiment detailed in this paper show how our method improves on other widely used techniques, and how it outperforms these techniques for noisy images.

We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical high-contrast imaging. Our package relies on the extensive python stack of scientific libraries and aims to provide a flexible framework for high-contrast data and image processing. In this paper, we describe the capabilities of VIP related to processing image sequences acquired using the angular differential imaging (ADI) observing technique. VIP implements functionalities for building high-contrast data processing pipelines, encompassing pre- and post-processing algorithms, potential source position and flux estimation, and sensitivity curve generation. Among the reference point-spread function subtraction techniques for ADI post-processing, VIP includes several flavors of principal component analysis (PCA) based algorithms, such as annular PCA and incremental PCA algorithms capable of processing big datacubes (of several gigabytes) on a computer with limited memory. Also, we present a novel ADI algorithm based on non-negative matrix factorization, which comes from the same family of low-rank matrix approximations as PCA and provides fairly similar results. We showcase the ADI capabilities of the VIP library using a deep sequence on HR 8799 taken with the LBTI/LMIRCam and its recently commissioned L-band vortex coronagraph. Using VIP, we investigated the presence of additional companions around HR 8799 and did not find any significant additional point source beyond the four known planets. VIP is available at http://github.com/vortex-exoplanet/VIP and is accompanied with Jupyter notebook tutorials illustrating the main functionalities of the library.

Background subtraction is usually based on low-level or hand-crafted features such as raw color components, gradients, or local binary patterns. As an improvement, we present a background subtraction algorithm based on spatial features learned with convolutional neural networks (ConvNets). Our algorithm uses a background model reduced to a single background image and a scene-specific training dataset to feed ConvNets that prove able to learn how to subtract the background from an input image patch. Experiments led on 2014 ChangeDetection.net dataset show that our ConvNet based algorithm at least reproduces the performance of state-of-the-art methods, and that it even outperforms them significantly when scene-specific knowledge is considered.

Motion detection plays an important role in most video based applications. One of the many possible ways to detect motion consists in background subtraction. This paper discusses experiments led for a particular background subtraction technique called ViBe. This technique models the background with a set of samples for each pixel and compares new frames, pixel by pixel, to determine if a pixel belongs to the background or to the foreground. In its original version, the scope of ViBe is limited to background modeling. In this paper, we introduce a series of modifications that alter the working of ViBe, like the inhibition of propagation around internal borders or the distinction between the updating and segmentation masks, or process the output, for example by some operations on the connected components. Experimental results obtained for video sequences provided on the workshop site validate the improvements of the proposed modifications.

Soccer videos are a rich playground for computer vision, involving many elements, such as players, lines, and specific objects. Hence, to capture the richness of this sport and allow for fine automated analyses, we release SoccerNet-v3, a major extension of the SoccerNet dataset, providing a wide variety of spatial annotations and cross-view correspondences. SoccerNet’s broadcast videos contain replays of important actions, allowing us to retrieve a same action from different viewpoints. We annotate those live and replay action frames showing same moments with exhaustive local information. Specifically, we label lines, goal parts, players, referees, teams, salient objects, jersey numbers, and we establish player correspondences between the views. This yields 1,324,732 annotations on 33,986 soccer images, making SoccerNet-v3 the largest dataset for multi-view soccer analysis. Derived tasks may benefit from these annotations, like camera calibration, player localization, team discrimination and multi-view re-identification, which can further sustain practical applications in augmented reality and soccer analytics. Finally, we provide Python codes to easily download our data and access our annotations.