Vehicle logo recognition based on deep learning architecture in video surveillance for intelligent traffic system

2013 ◽  
Author(s):  
Chun Pan ◽  
Mingxia Sun ◽  
Zhiguo Yan ◽  
Jie Shao ◽  
Xiaoming Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Video Surveillance ◽  
Traffic System ◽  
Logo Recognition ◽  
Intelligent Traffic System ◽  
Vehicle Logo Recognition

Vehicle logo recognition using whitening transformation and deep learning

2018 ◽  
Vol 13 (1) ◽  
pp. 111-119 ◽  
Author(s):  
Foo Chong Soon ◽  
Hui Ying Khaw ◽  
Joon Huang Chuah ◽  
Jeevan Kanesan
Keyword(s):  
Deep Learning ◽  
Logo Recognition ◽  
Vehicle Logo Recognition

Vehicle-logo recognition method based on image quality and PCA subspace

2010 ◽  
Vol 30 (8) ◽  
pp. 2244-2246
Author(s):  
Shao-qing MO ◽  
Zheng-guang LIU ◽  
Jun ZHANG
Keyword(s):  
Image Quality ◽  
Recognition Method ◽  
Logo Recognition ◽  
Vehicle Logo Recognition

Patch-CNN: Deep learning for logo detection and brand recognition

2021 ◽  
pp. 1-14
Author(s):  
Waqas Yousaf ◽  
Arif Umar ◽  
Syed Hamad Shirazi ◽  
Zakir Khan ◽  
Imran Razzak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Ground Truth ◽  
Logo Recognition ◽  
Brand Recognition ◽  
Detection And Recognition

Automatic logo detection and recognition is significantly growing due to the increasing requirements of intelligent documents analysis and retrieval. The main problem to logo detection is intra-class variation, which is generated by the variation in image quality and degradation. The problem of misclassification also occurs while having tiny logo in large image with other objects. To address this problem, Patch-CNN is proposed for logo recognition which uses small patches of logos for training to solve the problem of misclassification. The classification is accomplished by dividing the logo images into small patches and threshold is applied to drop no logo area according to ground truth. The architectures of AlexNet and ResNet are also used for logo detection. We propose a segmentation free architecture for the logo detection and recognition. In literature, the concept of region proposal generation is used to solve logo detection, but these techniques suffer in case of tiny logos. Proposed CNN is especially designed for extracting the detailed features from logo patches. So far, the technique has attained accuracy equals to 0.9901 with acceptable training and testing loss on the dataset used in this work.

scholarly journals Automatic Handgun Detection with Deep Learning in Video Surveillance Images

2021 ◽  
Vol 11 (13) ◽  
pp. 6085
Author(s):  
Jesus Salido ◽  
Vanesa Lomas ◽  
Jesus Ruiz-Santaquiteria ◽  
Oscar Deniz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Automatic Detection ◽  
Public Spaces ◽  
Detection Methods ◽  
Training Dataset ◽  
Average Precision ◽  
Terrorist Acts

There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training.

Deep Learning in Smart Video Surveillance for Crowd Management: A Systematic Literature Review

2021 ◽  
Author(s):  
Andrea Camille Garcia ◽  
Jealine Eleanor Gorre ◽  
Joshua Angelo Karl Perez ◽  
Mary Jane Samonte
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Video Surveillance ◽  
Systematic Literature Review ◽  
Crowd Management
Sign in / Sign up

Export Citation Format

Share Document