scholarly journals Privacy-Oriented Successive Approximation Image Position Follower Processing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ying Miao ◽  
Danyang Shao ◽  
Zhimin Yan
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Successive Approximation ◽  
Pose Estimation ◽  
Human Body ◽  
Estimation Method ◽  
Correlation Filter ◽  
Human Body Model ◽  
Dynamic Background ◽  
Key Points

In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm.

scholarly journals The Aircraft Pose Estimation Based on a Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Daoyong Fu ◽  
Wei Li ◽  
Songchen Han ◽  
Xinyan Zhang ◽  
Zhaohuan Zhan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Pose Estimation ◽  
Estimation Method ◽  
Human Pose Estimation ◽  
Two Dimensional ◽  
Surveillance Video ◽  
Key Points ◽  
Human Pose

The pose estimation of the aircraft in the airport plays an important role in preventing collisions and constructing the real-time scene of the airport. However, current airport target surveillance methods regard the aircraft as a point, neglecting the importance of pose estimation. Inspired by human pose estimation, this paper presents an aircraft pose estimation method based on a convolutional neural network through reconstructing the two-dimensional skeleton of an aircraft. Firstly, the key points of an aircraft and the matching relationship are defined to design a 2D skeleton of an aircraft. Secondly, a convolutional neural network is designed to predict all key points and components of the aircraft kept in the confidence maps and the Correlation Fields, respectively. Thirdly, all key points are coarsely matched based on the matching relationship and then refined through the Correlation Fields. Finally, the 2D skeleton of an aircraft is reconstructed. To overcome the lack of benchmark dataset, the airport surveillance video and Autodesk 3ds Max are utilized to build two datasets. Experiment results show that the proposed method get better performance in terms of accuracy and efficiency compared with other related methods.

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

scholarly journals Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

2021 ◽  
Author(s):  
Dengqing Tang ◽  
Lincheng Shen ◽  
Xiaojiao Xiang ◽  
Han Zhou ◽  
Tianjiang Hu
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Vision System ◽  
Estimation Method ◽  
Satellite System ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
The Impact ◽  
Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

Object Classification and Tracking in Real Time

2017 ◽  
pp. 1349-1394
Author(s):  
Amlan Jyoti Das ◽  
Navajit Saikia ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Feature Extraction ◽  
Mobile Robot ◽  
Object Tracking ◽  
Real Time ◽  
Traffic Control ◽  
Automatic Processing ◽  
Surveillance Systems ◽  
Classification Methods ◽  
Dynamic Background ◽  
Feature Extraction And Selection

Algorithms for automatic processing of visual data have been a topic of interest since the last few decades. Object tracking and classification methods are highly demanding in vehicle traffic control systems, surveillance systems for detecting unauthorized movement of vehicle and human, mobile robot applications, animal tracking, etc. There are still many challenging issues while dealing with dynamic background, occlusion, etc. in real time. This chapter presents an overview of various existing techniques for object detection, classification and tracking. As the most important requirements of tracking and classification algorithms are feature extraction and selection, different feature types are also included.

scholarly journals An RGB-D-Based Cross-Field of View Pose Estimation System for a Free Flight Target in a Wind Tunnel

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sheng Liu ◽  
Yuan Feng ◽  
Kang Shen ◽  
Yangqing Wang ◽  
Shengyong Chen
Keyword(s):  
Wind Tunnel ◽  
Real Time ◽  
Pose Estimation ◽  
Dynamic Testing ◽  
Estimation Method ◽  
Spatial Relationship ◽  
Field Of View ◽  
Free Flight ◽  
The Real ◽  
Estimation System

Estimating the real-time pose of a free flight aircraft in a complex wind tunnel environment is extremely difficult. Due to the high dynamic testing environment, complicated illumination condition, and the unpredictable motion of target, most general pose estimating methods will fail. In this paper, we introduce a cross-field of view (FOV) real-time pose estimation system, which provides high precision pose estimation of the free flight aircraft in the wind tunnel environment. Multiview live RGB-D streams are used in the system as input to ensure the measurement area can be fully covered. First, a multimodal initialization method is developed to measure the spatial relationship between the RGB-D camera and the aircraft. Based on all the input multimodal information, a so-called cross-FOV model is proposed to recognize the dominating sensor and accurately extract the foreground region in an automatic manner. Second, we develop an RGB-D-based pose estimation method for a single target, by which the 3D sparse points and the pose of the target can be simultaneously obtained in real time. Many experiments have been conducted, and an RGB-D image simulation based on 3D modeling is implemented to verify the effectiveness of our algorithm. Both the real scene’s and simulation scene’s experimental results demonstrate the effectiveness of our method.

Real-Time Human Body Pose Estimation for In-Car Depth Images

2019 ◽  
pp. 169-182
Author(s):  
Helena R. Torres ◽  
Bruno Oliveira ◽  
Jaime Fonseca ◽  
Sandro Queirós ◽  
João Borges ◽  
...  
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Human Body ◽  
Depth Images ◽  
Body Pose Estimation

Human Body Pose Estimation with Particle Swarm Optimisation

2008 ◽  
Vol 16 (4) ◽  
pp. 509-528 ◽  
Author(s):  
Špela Ivekovič ◽  
Emanuele Trucco ◽  
Yvan R. Petillot
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
Particle Swarm ◽  
Search Space ◽  
The Body ◽  
Particle Swarm Optimisation ◽  
Human Body Model ◽  
Still Images ◽  
Body Pose Estimation

In this paper we address the problem of human body pose estimation from still images. A multi-view set of images of a person sitting at a table is acquired and the pose estimated. Reliable and efficient pose estimation from still images represents an important part of more complex algorithms, such as tracking human body pose in a video sequence, where it can be used to automatically initialise the tracker on the first frame. The quality of the initialisation influences the performance of the tracker in the subsequent frames. We formulate the body pose estimation as an analysis-by-synthesis optimisation algorithm, where a generic 3D human body model is used to illustrate the pose and the silhouettes extracted from the images are used as constraints. A simple test with gradient descent optimisation run from randomly selected initial positions in the search space shows that a more powerful optimisation method is required. We investigate the suitability of the Particle Swarm Optimisation (PSO) for solving this problem and compare its performance with an equivalent algorithm using Simulated Annealing (SA). Our tests show that the PSO outperforms the SA in terms of accuracy and consistency of the results, as well as speed of convergence.

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