scholarly journals Real-Time Pedestrian Detection with Deep Network Cascades

2015 ◽  
Author(s):  
Anelia Angelova ◽  
Alex Krizhevsky ◽  
Vincent Vanhoucke ◽  
Abhijit Ogale ◽  
Dave Ferguson
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Deep Network

A robust and fast multispectral pedestrian detection deep network

2021 ◽  
pp. 106990
Author(s):  
Lu Ding ◽  
Yong Wang ◽  
Robert Laganière ◽  
Dan Huang ◽  
Xinbin Luo ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Deep Network

EPNet: Efficient Patch-based Deep Network for Real-Time Semantic Segmentation

2020 ◽  
Author(s):  
Sheshang Degadwala ◽  
Utsho Chakraborty ◽  
Sowrav Saha ◽  
Haimanti Biswas ◽  
Dhairya Vyas
Keyword(s):  
Real Time ◽  
Semantic Segmentation ◽  
Deep Network

scholarly journals Building Feature Pool Effectively for Real-Time Pedestrian Detection

2018 ◽  
Vol 1069 ◽  
pp. 012107
Author(s):  
Jijun Yang ◽  
Yingdong Ma ◽  
Zhibin Zhang
Keyword(s):  
Real Time ◽  
Pedestrian Detection

scholarly journals Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

2021 ◽  
Vol 11 (22) ◽  
pp. 10540
Author(s):  
Navjot Rathour ◽  
Zeba Khanam ◽  
Anita Gehlot ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Input Device ◽  
Facial Emotions ◽  
Deep Network

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

scholarly journals Real-time Pedestrian Detection Algorithm Based on Improved YOLOv3

2021 ◽  
Vol 2002 (1) ◽  
pp. 012075
Author(s):  
Xianchang Xi ◽  
Zhikai Huang ◽  
Lingyi Ning ◽  
Yang Zhang
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Detection Algorithm
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