230,524 research outputs found
A cookbook for temporal conceptual data modelling with description logic
We design temporal description logics suitable for reasoning about temporal conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. In the temporal dimension, they capture future and past temporal operators on concepts, flexible and rigid roles, the operators `always' and `some time' on roles, data assertions for particular moments of time and global concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,<), satisfying the constant domain assumption. We prove that the most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turn out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions we obtain logics whose complexity ranges between PSpace and NLogSpace. These positive results were obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
With efficient appearance learning models, Discriminative Correlation Filter
(DCF) has been proven to be very successful in recent video object tracking
benchmarks and competitions. However, the existing DCF paradigm suffers from
two major issues, i.e., spatial boundary effect and temporal filter
degradation. To mitigate these challenges, we propose a new DCF-based tracking
method. The key innovations of the proposed method include adaptive spatial
feature selection and temporal consistent constraints, with which the new
tracker enables joint spatial-temporal filter learning in a lower dimensional
discriminative manifold. More specifically, we apply structured spatial
sparsity constraints to multi-channel filers. Consequently, the process of
learning spatial filters can be approximated by the lasso regularisation. To
encourage temporal consistency, the filter model is restricted to lie around
its historical value and updated locally to preserve the global structure in
the manifold. Last, a unified optimisation framework is proposed to jointly
select temporal consistency preserving spatial features and learn
discriminative filters with the augmented Lagrangian method. Qualitative and
quantitative evaluations have been conducted on a number of well-known
benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and
VOT2018. The experimental results demonstrate the superiority of the proposed
method over the state-of-the-art approaches
TrackAgent: 6D Object Tracking via Reinforcement Learning
Tracking an object's 6D pose, while either the object itself or the observing
camera is moving, is important for many robotics and augmented reality
applications. While exploiting temporal priors eases this problem,
object-specific knowledge is required to recover when tracking is lost. Under
the tight time constraints of the tracking task, RGB(D)-based methods are often
conceptionally complex or rely on heuristic motion models. In comparison, we
propose to simplify object tracking to a reinforced point cloud (depth only)
alignment task. This allows us to train a streamlined approach from scratch
with limited amounts of sparse 3D point clouds, compared to the large datasets
of diverse RGBD sequences required in previous works. We incorporate temporal
frame-to-frame registration with object-based recovery by frame-to-model
refinement using a reinforcement learning (RL) agent that jointly solves for
both objectives. We also show that the RL agent's uncertainty and a
rendering-based mask propagation are effective reinitialization triggers.Comment: International Conference on Computer Vision Systems (ICVS) 202
4D Unsupervised Object Discovery
Object discovery is a core task in computer vision. While fast progresses
have been made in supervised object detection, its unsupervised counterpart
remains largely unexplored. With the growth of data volume, the expensive cost
of annotations is the major limitation hindering further study. Therefore,
discovering objects without annotations has great significance. However, this
task seems impractical on still-image or point cloud alone due to the lack of
discriminative information. Previous studies underlook the crucial temporal
information and constraints naturally behind multi-modal inputs. In this paper,
we propose 4D unsupervised object discovery, jointly discovering objects from
4D data -- 3D point clouds and 2D RGB images with temporal information. We
present the first practical approach for this task by proposing a ClusterNet on
3D point clouds, which is jointly iteratively optimized with a 2D localization
network. Extensive experiments on the large-scale Waymo Open Dataset suggest
that the localization network and ClusterNet achieve competitive performance on
both class-agnostic 2D object detection and 3D instance segmentation, bridging
the gap between unsupervised methods and full supervised ones. Codes and models
will be made available at https://github.com/Robertwyq/LSMOL.Comment: Accepted by NeurIPS 2022. 17 pages, 6 figure
Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity.
A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists
of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple
hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints
on the spatio-temporal continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results
in an annotation that is significantly more accurate than what would be obtained
by frame-by-frame evaluation of the classifier output. The framework has been implemented
and applied successfully to the analysis of team sports with a single
camera.
Key words: Visua
Tailoring temporal description logics for reasoning over temporal conceptual models
Temporal data models have been used to describe how data can evolve in the context of temporal databases. Both the Extended Entity-Relationship (EER) model and the Unified Modelling Language (UML) have been temporally extended to design temporal databases. To automatically check quality properties of conceptual schemas various encoding to Description Logics (DLs) have been proposed in the literature. On the other hand, reasoning on temporally extended DLs turn out to be too complex for effective reasoning ranging from 2ExpTime up to undecidable languages. We propose here to temporalize the ālight-weightā DL-Lite logics obtaining nice computational results while still being able to represent various constraints of temporal conceptual models. In particular, we consider temporal extensions of DL-Lite^N_bool, which was shown to be adequate for capturing non-temporal conceptual models without relationship inclusion, and its fragment DL-Lite^N_core with most primitive concept inclusions, which are nevertheless enough to represent almost all types of atemporal constraints (apart from
covering)
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