Visual Tracking with Multilevel Sparse Representation and Metric Learning

2018 ◽  
Vol 11 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Baifan Chen ◽  
Meng Peng ◽  
Lijue Liu ◽  
Tao Lu
Keyword(s):  
Sparse Representation ◽  
Visual Tracking ◽  
Hierarchical Structure ◽  
Real World ◽  
State Of The Art ◽  
Metric Learning ◽  
The State ◽  
Discriminative Ability ◽  
The Real ◽  
The Hierarchical Structure

Visual tracking arises in various real-world tasks where an object should be located in a video. Sparse representation can implement tracking problems by linearly representing object with a few templates. However, this approach has two main shortcomings. Namely, setting the templates updating frequency is difficult and meanwhile it is relatively weak in distinguishing the object from the background. For solving these problems, the author models a multilevel object template set that can be stratified by different updating time spans. The hierarchical structure and updating strategy promise the real-timeness, stability, and diversity of object template. Additionally, metric learning is combined to evaluate the object candidates and thereby improve the discriminative ability. Experiments on well-known visual tracking datasets demonstrate that the proposed method can track an object more robustly and accurately compared to the state-of-the-art approaches.

scholarly journals Real-World Robot Evolution: Why Would it (not) Work?

2021 ◽  
Vol 8 ◽  
Author(s):  
A.E. Eiben
Keyword(s):  
Real World ◽  
Current Practice ◽  
State Of The Art ◽  
The State ◽  
Evolutionary System ◽  
The Real ◽  
Robot Systems

This paper takes a critical look at the concept of real-world robot evolution discussing specific challenges for making it practicable. After a brief review of the state of the art several enablers are discussed in detail. It is noted that sample efficient evolution is one of the key prerequisites and there are various promising directions towards this in different stages of maturity, including learning as part of the evolutionary system, genotype filtering, and hybridizing real-world evolution with simulations in a new way. Furthermore, it is emphasized that an evolutionary system that works in the real world needs robots that work in the real world. Obvious as it may seem, to achieve this significant complexification of the robots and their tasks is needed compared to the current practice. Finally, the importance of not only building but also understanding evolving robot systems is emphasised, stating that in order to have the technology work we also need the science behind it.

scholarly journals Deep LSTM for Emoji Twitter Sentiment Exploration via Distributed Embeddings

2019 ◽  
Vol 8 (11) ◽  
pp. 1714-1718 ◽  
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Public Relations ◽  
Real World ◽  
State Of The Art ◽  
The State ◽  
Digital Marketing ◽  
Emotional Status ◽  
The Real ◽  
Proposed Model

Social media’s sentimental data is the most vital digital marketing platform that can help us to reveal the real world events including qualitative insights to understand people’s visibility about brands, politics, emotional status, and so on. With today’s interrelated world, a public relations disaster can be initiated with one post or a tweet. Conventional sentimental analysis is the process of defining whether the shared post on social media is neutral, positive or negative and has been focused by the Dealers, Administrations to understand public feelings of their products and corporation. However, extensive usage of emoji in social media has attracted an increasing interest. In this proposed framework, we suggest a novel scheme for Twitter sentiment method on emojis by considering pre-trained word and emoji embeddings. We first train our model to learn word, emoji embeddings under positive and negative tweets; later a classifier passes them through a neural network combining LSTM to achieve better performance. Our tests show that the proposed model operational for extracting sentiment-aware emojis and outperforms the state-of-the-art simulations.

A Survey of Real-Time Industrial Wireless LAN Research

2015 ◽  
Vol 738-739 ◽  
pp. 1105-1110 ◽  
Author(s):  
Yuan Qing Qin ◽  
Ying Jie Cheng ◽  
Chun Jie Zhou
Keyword(s):  
Real Time ◽  
Future Development ◽  
Wireless Lan ◽  
State Of The Art ◽  
The State ◽  
Local Networks ◽  
The Real ◽  
Time Requirements ◽  
And Performance ◽  
Fundamental Mechanism

This paper mainly surveys the state-of-the-art on real-time communicaton in industrial wireless local networks(WLANs), and also identifys the suitable approaches to deal with the real-time requirements in future. Firstly, this paper summarizes the features of industrial WLANs and the challenges it encounters. Then according to the real-time problems of industrial WLAN, the fundamental mechanism of each recent representative resolution is analyzed in detail. Meanwhile, the characteristics and performance of these resolutions are adequately compared. Finally, this paper concludes the current of the research and discusses the future development of industrial WLANs.

scholarly journals Particle Swarm Contour Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 407 ◽  
Author(s):  
Dominik Weikert ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Image Processing ◽  
Real World ◽  
State Of The Art ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Local Information ◽  
The State ◽  
Complete Knowledge ◽  
Real World Applications

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

scholarly journals Exploiting Visibility Information in Surface Reconstruction to Preserve Weakly Supported Surfaces

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Michal Jancosek ◽  
Tomas Pajdla
Keyword(s):  
Surface Reconstruction ◽  
Free Space ◽  
State Of The Art ◽  
The State ◽  
Real Object ◽  
Reconstruction Method ◽  
The Real ◽  
Model Free ◽  
Novel Method ◽  
Different Levels

We present a novel method for 3D surface reconstruction from an input cloud of 3D points augmented with visibility information. We observe that it is possible to reconstruct surfaces that do not contain input points. Instead of modeling the surface from input points, we model free space from visibility information of the input points. The complement of the modeled free space is considered full space. The surface occurs at interface between the free and the full space. We show that under certain conditions a part of the full space surrounded by the free space must contain a real object also when the real object does not contain any input points; that is, an occluder reveals itself through occlusion. Our key contribution is the proposal of a new interface classifier that can also detect the occluder interface just from the visibility of input points. We use the interface classifier to modify the state-of-the-art surface reconstruction method so that it gains the ability to reconstruct weakly supported surfaces. We evaluate proposed method on datasets augmented with different levels of noise, undersampling, and amount of outliers. We show that the proposed method outperforms other methods in accuracy and ability to reconstruct weakly supported surfaces.

scholarly journals Part-Based Visual Tracking via Online Weighted P-N Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Heng Fan ◽  
Jinhai Xiang ◽  
Jun Xu ◽  
Honghong Liao
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
The State ◽  
Tracking Algorithm ◽  
Local Feature ◽  
Experimental Results ◽  
Learning Method ◽  
Target Model ◽  
Tracking Process

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.

scholarly journals Scene text removal via cascaded text stroke detection and erasing

2021 ◽  
Vol 8 (2) ◽  
pp. 273-287
Author(s):  
Xuewei Bian ◽  
Chaoqun Wang ◽  
Weize Quan ◽  
Juntao Ye ◽  
Xiaopeng Zhang ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Processing Unit ◽  
Final Model ◽  
Scene Text ◽  
End To End

AbstractRecent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant results. In this work, a novel end-to-end framework is proposed based on accurate text stroke detection. Specifically, the text removal problem is decoupled into text stroke detection and stroke removal; we design separate networks to solve these two subproblems, the latter being a generative network. These two networks are combined as a processing unit, which is cascaded to obtain our final model for text removal. Experimental results demonstrate that the proposed method substantially outperforms the state-of-the-art for locating and erasing scene text. A new large-scale real-world dataset with 12,120 images has been constructed and is being made available to facilitate research, as current publicly available datasets are mainly synthetic so cannot properly measure the performance of different methods.

scholarly journals Waste generation prediction under uncertainty in smart cities through deep neuroevolution

2019 ◽  
pp. 128-138 ◽  
Author(s):  
Andrés Camero ◽  
Jamal Toutouh ◽  
Javier Ferrer ◽  
Enrique Alba
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Smart Cities ◽  
Uncertain Data ◽  
Recurrent Network ◽  
The State ◽  
Waste Generation ◽  
Waste Collection ◽  
The Way

The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.

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