Transferring priors from virtual data for crowd counting in real world

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Xiaoheng Jiang ◽  
Hao Liu ◽  
Li Zhang ◽  
Geyang Li ◽  
Mingliang Xu ◽  
...  
Keyword(s):  
Real World ◽  
Crowd Counting ◽  
Virtual Data

scholarly journals Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning

2021 ◽  
Vol 11 (24) ◽  
pp. 12037
Author(s):  
Xiaoyu Hou ◽  
Jihui Xu ◽  
Jinming Wu ◽  
Huaiyu Xu
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Density Estimation ◽  
Real World ◽  
Domain Adaptation ◽  
Manual Annotation ◽  
Crowd Counting ◽  
Cross Domain ◽  
Meta Learning ◽  
Density Map

Counting people in crowd scenarios is extensively conducted in drone inspections, video surveillance, and public safety applications. Today, crowd count algorithms with supervised learning have improved significantly, but with a reliance on a large amount of manual annotation. However, in real world scenarios, different photo angles, exposures, location heights, complex backgrounds, and limited annotation data lead to supervised learning methods not working satisfactorily, plus many of them suffer from overfitting problems. To address the above issues, we focus on training synthetic crowd data and investigate how to transfer information to real-world datasets while reducing the need for manual annotation. CNN-based crowd-counting algorithms usually consist of feature extraction, density estimation, and count regression. To improve the domain adaptation in feature extraction, we propose an adaptive domain-invariant feature extracting module. Meanwhile, after taking inspiration from recent innovative meta-learning, we present a dynamic-β MAML algorithm to generate a density map in unseen novel scenes and render the density estimation model more universal. Finally, we use a counting map refiner to optimize the coarse density map transformation into a fine density map and then regress the crowd number. Extensive experiments show that our proposed domain adaptation- and model-generalization methods can effectively suppress domain gaps and produce elaborate density maps in cross-domain crowd-counting scenarios. We demonstrate that the proposals in our paper outperform current state-of-the-art techniques.

scholarly journals Applications of Virtual Data in Subsea Inspections

2020 ◽  
Vol 8 (5) ◽  
pp. 328 ◽  
Author(s):  
Michael O’Byrne ◽  
Bidisha Ghosh ◽  
Franck Schoefs ◽  
Vikram Pakrashi
Keyword(s):  
Virtual Environments ◽  
Real World ◽  
Detection Methods ◽  
Current State ◽  
First Case ◽  
Wide Range ◽  
Inspection Process ◽  
State Of Research ◽  
Virtual Data

This paper investigates the role that virtual environments can play in assisting engineers and divers when performing subsea inspections. We outline the current state of research and technology that is relevant to the development of effective virtual environments. Three case studies are presented demonstrating how the inspection process can be enhanced through the use of virtual data. The first case study looks at how immersive virtual underwater scenes can be created to help divers and inspectors plan and implement real-world inspections. The second case study shows an example where deep learning-based computer vision methods are trained on datasets comprised of instances of virtual damage, specifically instances of barnacle fouling on the surface of a ship hull. The trained deep models are then applied to detect real-world instances of biofouling with promising results. The final case study shows how image-based damage detection methods can be calibrated using virtual images of damage captured under various simulated levels of underwater visibility. The work emphasizes the value of virtual data in creating a more efficient, safe and informed underwater inspection campaign for a wide range of built infrastructure, potentially leading to better monitoring, inspection and lifetime performance of such underwater structures.

scholarly journals Crowd Counting using Deep Recurrent Spatial-Aware Network

2018 ◽  
Author(s):  
Lingbo Liu ◽  
Hongjun Wang ◽  
Guanbin Li ◽  
Wanli Ouyang ◽  
Liang Lin
Keyword(s):  
Neural Network ◽  
Real World ◽  
Local Refinement ◽  
Crowd Counting ◽  
Multi Scale ◽  
Residual Learning ◽  
Crowd Density ◽  
Real World Applications ◽  
Refinement Process ◽  
Density Map

Crowd counting from unconstrained scene images is a crucial task in many real-world applications like urban surveillance and management, but it is greatly challenged by the camera’s perspective that causes huge appearance variations in people’s scales and rotations. Conventional methods address such challenges by resorting to fixed multi-scale architectures that are often unable to cover the largely varied scales while ignoring the rotation variations. In this paper, we propose a unified neural network framework, named Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues in a learnable spatial transform module with a region-wise refinement process. Specifically, our framework incorporates a Recurrent Spatial-Aware Refinement (RSAR) module iteratively conducting two components: i) a Spatial Transformer Network that dynamically locates an attentional region from the crowd density map and transforms it to the suitable scale and rotation for optimal crowd estimation; ii) a Local Refinement Network that refines the density map of the attended region with residual learning. Extensive experiments on four challenging benchmarks show the effectiveness of our approach. Specifically, comparing with the existing best-performing methods, we achieve an improvement of 12\% on the largest dataset WorldExpo’10 and 22.8\% on the most challenging dataset UCF\_CC\_50

Replications can cause distorted belief in scientific progress

2018 ◽  
Vol 41 ◽  
Author(s):  
Michał Białek
Keyword(s):  
Real World ◽  
Scientific Progress ◽  
The Public ◽  
Psychological Science

AbstractIf we want psychological science to have a meaningful real-world impact, it has to be trusted by the public. Scientific progress is noisy; accordingly, replications sometimes fail even for true findings. We need to communicate the acceptability of uncertainty to the public and our peers, to prevent psychology from being perceived as having nothing to say about reality.

Current Issues: Evidence-Based Practice for the Real World: Thoughts on Clinical Education in Communication Disorders

2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe
Keyword(s):  
Clinical Education ◽  
Real World ◽  
Evidence Based Practice ◽  
Communication Disorders ◽  
University Faculty ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Basic Principles ◽  
The Real ◽  
Language Pathology

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.

Qualitative Case Study Research Design

2015 ◽  
Vol 25 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Jennifer Tetnowski
Keyword(s):  
Research Design ◽  
Real World ◽  
Qualitative Case Study ◽  
Case Study Research ◽  
Study Research

Qualitative case study research can be a valuable tool for answering complex, real-world questions. This method is often misunderstood or neglected due to a lack of understanding by researchers and reviewers. This tutorial defines the characteristics of qualitative case study research and its application to a broader understanding of stuttering that cannot be defined through other methodologies. This article will describe ways that data can be collected and analyzed.

The Real-World Fellowship

2006 ◽  
Vol 40 (7) ◽  
pp. 47
Author(s):  
LEE SAVIO BEERS
Keyword(s):  
Real World ◽  
The Real

Clobex Spray Improves Psoriasis, Quality of Life in Real-World Study

2007 ◽  
Vol 38 (4) ◽  
pp. 26
Author(s):  
DAMIAN MCNAMARA
Keyword(s):  
Quality Of Life ◽  
Real World
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