scholarly journals Target Tracking via Particle Filter and Convolutional Network

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hongxia Chu ◽  
Kejun Wang ◽  
Xianglei Xing
Keyword(s):  
Particle Filter ◽  
Observation Model ◽  
Partial Occlusion ◽  
Efficient Design ◽  
Complex Environment ◽  
Complex Scene ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Global Representation ◽  
Particle Filter Algorithm

We propose a more effective tracking algorithm which can work robustly in a complex scene such as illumination, appearance change, and partial occlusion. The algorithm is based on an improved particle filter which used the efficient design of observation model. Predefined convolutional filters are used to extract the high-order features. The global representation is generated by combining local features without changing their structures and space arrangements. It not only increases the feature invariance, but also maintains the specificity. The extracted feature from convolution network is introduced into particle filter algorithm. The observation model is constructed by fusing the color feature of the target and a set of features from templates which are extracted by convolutional networks without training in our paper. It is fused with the features extracted from convolutional network for tracking. In the process of tracking, the template is updated in real time, and then the robustness of the algorithm is improved. Experiments show that the algorithm can achieve an ideal tracking effect when the targets are in a complex environment.

scholarly journals Vehicle Tracking Algorithm Based on Observation Feedback and Block Symmetry Particle Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yanshuang Hao ◽  
Yixin Yin ◽  
Jinhui Lan
Keyword(s):  
Particle Filter ◽  
State Transition ◽  
Vehicle Tracking ◽  
Transition Model ◽  
Observation Data ◽  
Observation Model ◽  
Partial Occlusion ◽  
Proposal Distribution ◽  
State Transition Model ◽  
Particle Filter Algorithm

This paper proposes a novel particle filter algorithm for vehicle tracking, which feeds observation information back to state model and integrates block symmetry into observation model. In view of the proposal distribution in traditional particle filter without considering the observation data, a new state transition model which takes the observation into account is presented, so that the allocation of particles is more familiar with the posterior distribution. To track the vehicles in background with similar colors or under partial occlusion, block symmetry is proposed and introduced into the observation model. Experimental results show that the proposed algorithm can improve the accuracy and robustness of vehicle tracking compared with traditional particle filter and Kernel Particle Filter.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

scholarly journals Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Adaptive Filtering ◽  
Divide And Conquer ◽  
Particle Swarm Algorithm ◽  
Tracking Accuracy ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

A quick evaluation method for the lifetime of the fuel cell MEA with the particle filter algorithm

2021 ◽  
pp. 1-14
Author(s):  
Luyan He ◽  
Zhigang Zhan ◽  
Hong Chen ◽  
Panxing Jiang ◽  
Yuan Yu ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Particle Filter ◽  
Evaluation Method ◽  
Particle Filter Algorithm

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Shengsheng Qian ◽  
Jun Hu ◽  
Quan Fang ◽  
Changsheng Xu
Keyword(s):  
Social Media ◽  
Visual Information ◽  
Representation Learning ◽  
Fake News ◽  
Unified Framework ◽  
Model Learning ◽  
Convolutional Network ◽  
Textual Information ◽  
Convolutional Networks ◽  
Real World Datasets

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

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