74 research outputs found
Ghost-removal image warping for optical flow estimation
Traditional image warping methods used in optical flow estimation usually adopt simple interpolation strategies to obtain the warped images. But without considering the characteristic of occluded regions, the traditional methods may result in undesirable ghosting artifacts. To tackle this problem, in this paper we propose a novel image warping method to effectively remove ghosting artifacts. To be Specific, when given a warped image, the ghost regions are firstly discriminated using the optical flow information. Then, we use a new image compensation technique to eliminate the ghosting artifacts. The proposed method can avoid serious distortion in the warped images, therefore can prevent error propagation in the coarse-to-fine optical flow estimation schemes. Meanwhile, our approach can be easily integrated into various optical flow estimation methods. Experimental results on some popular datasets such as Flying Chairs and MPI-Sintel demonstrate that the proposed method can improve the performance of current optical flow estimation methods
Pattern memory analysis based on stability theory of cellular neural networks
AbstractIn this paper, several sufficient conditions are obtained to guarantee that the n-dimensional cellular neural network can have even (⩽2n) memory patterns. In addition, the estimations of attractive domain of such stable memory patterns are obtained. These conditions, which can be directly derived from the parameters of the neural networks, are easily verified. A new design procedure for cellular neural networks is developed based on stability theory (rather than the well-known perceptron training algorithm), and the convergence in the new design procedure is guaranteed by the obtained local stability theorems. Finally, the validity and performance of the obtained results are illustrated by two examples
Coordinated Multi-Agent Patrolling with History-Dependent Cost Rates -- Asymptotically Optimal Policies for Large-Scale Systems
We study a large-scale patrol problem with history-dependent costs and
multi-agent coordination, where we relax the assumptions on the past patrol
studies, such as identical agents, submodular reward functions and capabilities
of exploring any location at any time. Given the complexity and uncertainty of
the practical situations for patrolling, we model the problem as a
discrete-time Markov decision process (MDP) that consists of a large number of
parallel restless bandit processes and aim to minimize the cumulative
patrolling cost over a finite time horizon. The problem exhibits an excessively
large size of state space, which increases exponentially in the number of
agents and the size of geographical region for patrolling. We extend the
Whittle relaxation and Lagrangian dynamic programming (DP) techniques to the
patrolling case, where the additional, non-trivial constraints used to track
the trajectories of all the agents are inevitable and significantly complicate
the analysis. The past results cannot ensure the existence of patrol policies
with theoretically bounded performance degradation. We propose a patrol policy
applicable and scalable to the above mentioned large, complex problem. By
invoking Freidlin's theorem, we prove that the performance deviation between
the proposed policy and optimality diminishes exponentially in the problem
size.Comment: 37 pages, 4 figure
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
We rethink a well-know bottom-up approach for multi-person pose estimation
and propose an improved one. The improved approach surpasses the baseline
significantly thanks to (1) an intuitional yet more sensible representation,
which we refer to as body parts to encode the connection information between
keypoints, (2) an improved stacked hourglass network with attention mechanisms,
(3) a novel focal L2 loss which is dedicated to hard keypoint and keypoint
association (body part) mining, and (4) a robust greedy keypoint assignment
algorithm for grouping the detected keypoints into individual poses. Our
approach not only works straightforwardly but also outperforms the baseline by
about 15% in average precision and is comparable to the state of the art on the
MS-COCO test-dev dataset. The code and pre-trained models are publicly
available online.Comment: Accepted by AAAI 2020 (the Thirty-Fourth AAAI Conference on
Artificial Intelligence
Data Augmentation and Classification of Sea-Land Clutter for Over-the-Horizon Radar Using AC-VAEGAN
In the sea-land clutter classification of sky-wave over-the-horizon-radar
(OTHR), the imbalanced and scarce data leads to a poor performance of the deep
learning-based classification model. To solve this problem, this paper proposes
an improved auxiliary classifier generative adversarial network~(AC-GAN)
architecture, namely auxiliary classifier variational autoencoder generative
adversarial network (AC-VAEGAN). AC-VAEGAN can synthesize higher quality
sea-land clutter samples than AC-GAN and serve as an effective tool for data
augmentation. Specifically, a 1-dimensional convolutional AC-VAEGAN
architecture is designed to synthesize sea-land clutter samples. Additionally,
an evaluation method combining both traditional evaluation of GAN domain and
statistical evaluation of signal domain is proposed to evaluate the quality of
synthetic samples. Using a dataset of OTHR sea-land clutter, both the quality
of the synthetic samples and the performance of data augmentation of AC-VAEGAN
are verified. Further, the effect of AC-VAEGAN as a data augmentation method on
the classification performance of imbalanced and scarce sea-land clutter
samples is validated. The experiment results show that the quality of samples
synthesized by AC-VAEGAN is better than that of AC-GAN, and the data
augmentation method with AC-VAEGAN is able to improve the classification
performance in the case of imbalanced and scarce sea-land clutter samples.Comment: 13 pages, 16 figure
Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing
We address the challenge of tracking an unknown number of targets in strong
clutter environments using measurements from a radar sensor. Leveraging the
range-Doppler spectra information, we identify the measurement classes, which
serve as additional information to enhance clutter rejection and data
association, thus bolstering the robustness of target tracking. We first
introduce a novel neural enhanced message passing approach, where the beliefs
obtained by the unified message passing are fed into the neural network as
additional information. The output beliefs are then utilized to refine the
original beliefs. Then, we propose a classification-aided robust multiple
target tracking algorithm, employing the neural enhanced message passing
technique. This algorithm is comprised of three modules: a message-passing
module, a neural network module, and a Dempster-Shafer module. The
message-passing module is used to represent the statistical model by the factor
graph and infers target kinematic states, visibility states, and data
associations based on the spatial measurement information. The neural network
module is employed to extract features from range-Doppler spectra and derive
beliefs on whether a measurement is target-generated or clutter-generated. The
Dempster-Shafer module is used to fuse the beliefs obtained from both the
factor graph and the neural network. As a result, our proposed algorithm adopts
a model-and-data-driven framework, effectively enhancing clutter suppression
and data association, leading to significant improvements in multiple target
tracking performance. We validate the effectiveness of our approach using both
simulated and real data scenarios, demonstrating its capability to handle
challenging tracking scenarios in practical radar applications.Comment: 15 page
Joint State Estimation and Noise Identification Based on Variational Optimization
In this article, the state estimation problems with unknown process noise and
measurement noise covariances for both linear and nonlinear systems are
considered. By formulating the joint estimation of system state and noise
parameters into an optimization problem, a novel adaptive Kalman filter method
based on conjugate-computation variational inference, referred to as CVIAKF, is
proposed to approximate the joint posterior probability density function of the
latent variables. Unlike the existing adaptive Kalman filter methods utilizing
variational inference in natural-parameter space, CVIAKF performs optimization
in expectation-parameter space, resulting in a faster and simpler solution.
Meanwhile, CVIAKF divides optimization objectives into conjugate and
non-conjugate parts of nonlinear dynamical models, whereas conjugate
computations and stochastic mirror-descent are applied, respectively.
Remarkably, the reparameterization trick is used to reduce the variance of
stochastic gradients of the non-conjugate parts. The effectiveness of CVIAKF is
validated through synthetic and real-world datasets of maneuvering target
tracking.Comment: 13 page
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