scholarly journals Alternatives for Locating People Using Cameras and Embedded AI Accelerators: A Practical Approach

2021 ◽  
Vol 7 (1) ◽  
pp. 53
Author(s):  
Ángel Carro-Lagoa ◽  
Valentín Barral ◽  
Miguel González-López ◽  
Carlos J. Escudero ◽  
Luis Castedo
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Video Processing ◽  
Post Processing ◽  
Person Detection ◽  
Positioning Systems ◽  
Public Datasets ◽  
Localization And Tracking ◽  
Person Location ◽  
Estimation Models

Indoor positioning systems usually rely on RF-based devices that should be carried by the targets, which is non-viable in certain use cases. Recent advances in AI have increased the reliability of person detection in images, thus, enabling the use of surveillance cameras to perform person localization and tracking. This paper evaluates the performance of indoor person location using cameras and edge devices with AI accelerators. We describe the video processing performed in each edge device, including the selected AI models and the post-processing of their outputs to obtain the positions of the detected persons and allow their tracking. The person location is based on pose estimation models as they provide better results than do object detection networks in occlusion situations. Experimental results are obtained with public datasets to show the feasibility of the solution.

scholarly journals A New Edge Patch with Rotation Invariance for Object Detection and Pose Estimation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 887 ◽  
Author(s):  
Xunwei Tong ◽  
Ruifeng Li ◽  
Lianzheng Ge ◽  
Lijun Zhao ◽  
Ke Wang
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Rotation Invariance ◽  
Depth Image ◽  
Detection Accuracy ◽  
Edge Pixel ◽  
Local Patch ◽  
Background Clutter ◽  
Public Datasets ◽  
Local Sampling

Local patch-based methods of object detection and pose estimation are promising. However, to the best of the authors’ knowledge, traditional red-green-blue and depth (RGB-D) patches contain scene interference (foreground occlusion and background clutter) and have little rotation invariance. To solve these problems, a new edge patch is proposed and experimented with in this study. The edge patch is a local sampling RGB-D patch centered at the edge pixel of the depth image. According to the normal direction of the depth edge, the edge patch is sampled along a canonical orientation, making it rotation invariant. Through a process of depth detection, scene interference is eliminated from the edge patch, which improves the robustness. The framework of the edge patch-based method is described, and the method was evaluated on three public datasets. Compared with existing methods, the proposed method achieved a higher average F1-score (0.956) on the Tejani dataset and a better average detection rate (62%) on the Occlusion dataset, even in situations of serious scene interference. These results showed that the proposed method has higher detection accuracy and stronger robustness.

scholarly journals Object Detection And Autoencoder-Based 6d Pose Estimation For Highly Cluttered Bin Picking

2021 ◽  
Author(s):  
Timon Hofer ◽  
Faranak Shamsafar ◽  
Nuri Benbarka ◽  
Andreas Zell
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Bin Picking

DIB: Piled Man-made Object Detection and Pose Estimation in Point Cloud Blocks

2021 ◽  
Author(s):  
Weiqian Guo ◽  
Rendong Ying ◽  
Peilin Liu ◽  
Weihang Wang
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Point Cloud

In-Cabin vehicle synthetic data to test Deep Learning based human pose estimation models

2021 ◽  
Author(s):  
Luis Gustavo Tomal Ribas ◽  
Marta Pereira Cocron ◽  
Joed Lopes Da Silva ◽  
Alessandro Zimmer ◽  
Thomas Brandmeier
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Synthetic Data ◽  
Human Pose Estimation ◽  
Human Pose ◽  
Estimation Models

Enhanced Gaze Following via Object Detection and Human Pose Estimation

2019 ◽  
pp. 502-513 ◽  
Author(s):  
Jian Guan ◽  
Liming Yin ◽  
Jianguo Sun ◽  
Shuhan Qi ◽  
Xuan Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Gaze Following ◽  
Human Pose Estimation ◽  
Human Pose

scholarly journals Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yizhong Yang ◽  
Qiang Zhang ◽  
Pengfei Wang ◽  
Xionglou Hu ◽  
Nengju Wu
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model ◽  
Dynamic Background ◽  
Spatiotemporal Model ◽  
Temporal Features ◽  
Common Technique ◽  
Public Datasets

Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

Object Detection and Pose Estimation Approaches for Soldering Danger Detection

2021 ◽  
Author(s):  
Tomoya Yasunaga ◽  
Tetsuya Oda ◽  
Nobuki Saito ◽  
Aoto Hirata ◽  
Kyohei Toyoshima ◽  
...  
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Danger Detection
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