32 research outputs found
Frontal-view gait recognition by intra- and inter-frame rectangle size distribution
peer reviewedCurrent trends seem to accredit gait as a sensible biometric feature for human identification, at least in a
multimodal system. In addition to being a robust feature, gait is hard to fake and requires no cooperation
from the user. As in many video systems, the recognition confidence relies on the angle of view of the
camera and on the illumination conditions, inducing a sensitivity to operational conditions that one
may wish to lower.
In this paper we present an efficient approach capable of recognizing people in frontal-view video
sequences. The approach uses an intra-frame description of silhouettes which consists of a set of rectangles
that will fit into any closed silhouette. A dynamic, inter-frame, dimension is then added by aggregating
the size distributions of these rectangles over multiple successive frames. For each new frame, the
inter-frame gait signature is updated and used to estimate the identity of the person detected in the
scene. Finally, in order to smooth the decision on the identity, a majority vote is applied to previous
results. In the final part of this article, we provide experimental results and discuss the accuracy of the
classification for our own database of 21 known persons, and for a public database of 25 persons
ViBe: A universal background subtraction algorithm for video sequences
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 on 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 pseudocode 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. There is a dedicated web page for ViBe at http://www.telecom.ulg.ac.be/research/vibe
Dynamic NeRFs for Soccer Scenes
The long-standing problem of novel view synthesis has many applications,
notably in sports broadcasting. Photorealistic novel view synthesis of soccer
actions, in particular, is of enormous interest to the broadcast industry. Yet
only a few industrial solutions have been proposed, and even fewer that achieve
near-broadcast quality of the synthetic replays. Except for their setup of
multiple static cameras around the playfield, the best proprietary systems
disclose close to no information about their inner workings. Leveraging
multiple static cameras for such a task indeed presents a challenge rarely
tackled in the literature, for a lack of public datasets: the reconstruction of
a large-scale, mostly static environment, with small, fast-moving elements.
Recently, the emergence of neural radiance fields has induced stunning progress
in many novel view synthesis applications, leveraging deep learning principles
to produce photorealistic results in the most challenging settings. In this
work, we investigate the feasibility of basing a solution to the task on
dynamic NeRFs, i.e., neural models purposed to reconstruct general dynamic
content. We compose synthetic soccer environments and conduct multiple
experiments using them, identifying key components that help reconstruct soccer
scenes with dynamic NeRFs. We show that, although this approach cannot fully
meet the quality requirements for the target application, it suggests promising
avenues toward a cost-efficient, automatic solution. We also make our work
dataset and code publicly available, with the goal to encourage further efforts
from the research community on the task of novel view synthesis for dynamic
soccer scenes. For code, data, and video results, please see
https://soccernerfs.isach.be.Comment: Accepted at the 6th International ACM Workshop on Multimedia Content
Analysis in Sports. 8 pages, 9 figures. Project page:
https://soccernerfs.isach.b
A platform for the fast interpretation of movements and localization of users in 3D applications driven by a range camera
Interactivity is one of the key challenges for immersive applications like gaming. Manufacturers have been working towards interfaces that are driven by a device (e.g. a Wiimote) or interfaces
that are controlled by a camera with a subsequent computer vision module. Both approaches have unique advantages, but they do not permit to localize users in the scene with an appropriate accuracy.
Therefore, we propose to use both a range camera and accurate range sensors to enable the interpretation of movements.
This paper describes a platform that uses a range camera to acquire the silhouettes of users, regardless of illumination, and to improve the pose recovery with range information after some
image processing steps. In addition, to circumvent the difficult process of calibration required to map range values to physical distances, we complete the system with several range laser sensors. These sensors are located in a horizontal plane, and measure distances up to a few centimeters. We combine all these measurements to obtain a localization map, used to locate users in the scene at a negligible computational cost. Our method fills a gap in
3D applications that requires absolute positions.Peer reviewe
Observables in Topological Yang-Mills Theories With Extended Shift Supersymmetry
We present a complete classification, at the classical level, of the
observables of topological Yang-Mills theories with an extended shift
supersymmetry of N generators, in any space-time dimension. The observables are
defined as the Yang-Mills BRST cohomology classes of shift supersymmetry
invariants. These cohomology classes turn out to be solutions of an N-extension
of Witten's equivariant cohomology. This work generalizes results known in the
case of shift supersymmetry with a single generator.Comment: 27 pages, Late
An evaluation of pixel-based methods for the detection of floating objects on the sea surface
Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperformthe more conventional parametric techniques, with only
little prior assumptions about the physical properties of the scene
Observables in Topological Theories: A Superspace Formulation
Observables of topological Yang-Mills theory were defined by Witten as the
classes of an equivariant cohomology. We propose to define them alternatively
as the BRST cohomology classes of a superspace version of the theory, where
BRST invariance is associated to super Yang-Mills invariance. We provide and
discuss the general solution of this cohomology.Comment: Prepared for International Conference on Renormalization Group and
Anomalies in Gravity and Cosmology (IRGA 2003), Ouro Preto, MG, Brazil, 17-23
Mar 200
Dynamic NeRFs for Soccer Scenes
peer reviewedThe long-standing problem of novel view synthesis has many applications, notably in sports broadcasting. Photorealistic novel view synthesis of soccer actions, in particular, is of enormous interest to the broadcast industry. Yet only a few industrial solutions have been proposed, and even fewer that achieve near-broadcast quality of the synthetic replays. Except for their setup of multiple static cameras around the playfield, the best proprietary systems disclose close to no information about their inner workings. Leveraging multiple static cameras for such a task indeed presents a challenge rarely tackled in the literature, for a lack of public datasets: the reconstruction of a large-scale, mostly static environment, with small, fast-moving elements. Recently, the emergence of neural radiance fields has induced stunning progress in many novel view synthesis applications, leveraging deep learning principles to produce photorealistic results in the most challenging settings. In this work, we investigate the feasibility of basing a solution to the task on dynamic NeRFs, i.e., neural models purposed to reconstruct general dynamic content. We compose synthetic soccer environments and conduct multiple experiments using them, identifying key components that help reconstruct soccer scenes with dynamic NeRFs. We show that, although this approach cannot fully meet the quality requirements for the target application, it suggests promising avenues toward a cost-efficient, automatic solution. We also make our work dataset and code publicly available, with the goal to encourage further efforts from the research community on the task of novel view synthesis for dynamic soccer scenes. For code, data, and video results, please see https://soccernerfs.isach.be
Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting
peer reviewedSoccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera calibration and on its current limitations for the scientific community. More precisely, we tackle the absence of a large-scale calibration dataset and of a public calibration network trained on such a dataset. Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games. We further release our distilled network, and leverage it to provide 3 ways of representing the calibration results along with player localization. Finally, we exploit those representations within the current best architecture for the action spotting task of SoccerNet-v2, and achieve new state-of-the-art performances.DeepSpor
Finiteness of PST self-dual models
The Pasti-Sorokin-Tonin model for describing chiral forms is considered at
the quantum level. We study the ultraviolet and infrared behaviour of the model
in two, four and six dimensions in the framework of algebraic renormalization.
The absence of anomalies, as well as the finiteness, up to non-physical
renormalizations, are shown in all dimensions analyzed.Comment: 19 page