Health problems due to poor posture is becoming increasingly common among every age group in today's time. Sedentary workstyle, lack of physical exercise, unsupported or unbalanced sitting conditions are some of the factors that cause posture-related problems. The correct or inappropriate posture of the human body can be detected using a wearable sensor system, as discussed in this paper. In this work, three tri-axial accelerometers placed at the back of the subject were used to classify nine different postures in sitting and standing positions. A novel combined deep learning model consisting of a fully convolutional network (FCN) and long short-term model (LSTM) is proposed for posture classification. Experimental results show that combined FCN-LSTM network yields the highest average classification accuracy of 99.91%, whereas the individual FCN and LSTM networks yield classification accuracies of 97.88% and 88.47%, respectively. The models are also compared in terms of complexity and execution time.

Posture detection using Deep Learning for Time Series Data

Posture detection targets toward providing assessments for the monitoring of the health and welfare of humans have been of great interest to researchers from different disciplines. The use of computer vision systems for posture recognition might result in useful improvements in healthy aging and support for elderly people in their daily activities in the field of health care. Computer vision and pattern recognition communities are particularly interested in fall automated recognition. Human sensing and artificial intelligence have both paid great attention to human posture detection (HPD). The health status of elderly people can be remotely monitored using human posture detection, which can distinguish between positions such as standing, sitting, and walking. The most recent research identified posture using both deep learning (DL) and conventional machine learning (ML) classifiers. However, these techniques do not effectively identify the postures and overfits of the model overfits. Therefore, this study suggested a deep convolutional neural network (DCNN) framework to examine and classify human posture in health monitoring systems. This study proposes a feature selection technique, DCNN, and a machine learning technique to assess the previously mentioned problems. The InceptionV3 DCNN model is hybridized with SVM ML and its performance is compared. Furthermore, the performance of the proposed system is validated with other transfer learning (TL) techniques such as InceptionV3, DenseNet121, and ResNet50. This study uses the least absolute shrinkage and selection operator (LASSO)-based feature selection to enhance the feature vector. The study also used various techniques, such as data augmentation, dropout, and early stop, to overcome the problem of model overfitting. The performance of this DCNN framework is tested using benchmark Silhouettes of human posture and classification accuracy, loss, and AUC value of 95.42%, 0.01, and 99.35% are attained, respectively. Furthermore, the results of the proposed technology offer the most promising solution for indoor monitoring systems.

/pdf/hybrid-inceptionv3-svm-based-approach-for-human-posture-1vd0ea94.pdf

Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems

Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multi-national and multi-language detection, with either one-line or two-line layouts is presented. The YOLOv2 detector with ResNet feature extraction core was utilized for LP detection, and a new low complexity convolutional neural network architecture was proposed to classify LPs. Results show that the proposed approach achieves an average detection precision of 99.57%, whereas the country, language, and layout classification accuracy is 99.33%.

/pdf/multi-national-and-multi-language-license-plate-detection-4e9s8gsmdb.pdf

Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks

Character recognition is an important research field of interest for many applications. In recent years, deep learning has made breakthroughs in image classification, especially for character recognition. However, convolutional neural networks (CNN) still deliver state-of-the-art results in this area. Motivated by the success of CNNs, this paper proposes a simple novel full depth stacked CNN architecture for Latin and Arabic handwritten alphanumeric characters that is also utilized for license plate (LP) characters recognition. The proposed architecture is constructed by four convolutional layers, two max-pooling layers, and one fully connected layer. This architecture is low-complex, fast, reliable and achieves very promising classification accuracy that may move the field forward in terms of low complexity, high accuracy and full feature extraction. The proposed approach is tested on four benchmarks for handwritten character datasets, Fashion-MNIST dataset, public LP character datasets and a newly introduced real LP isolated character dataset. The proposed approach tests report an error of only 0.28% for MNIST, 0.34% for MAHDB, 1.45% for AHCD, 3.81% for AIA9K, 5.00% for Fashion-MNIST, 0.26% for Saudi license plate character and 0.97% for Latin license plate characters datasets. The license plate characters include license plates from Turkey (TR), Europe (EU), USA, United Arab Emirates (UAE) and Kingdom of Saudi Arabia (KSA).

Full depth CNN classifier for handwritten and license plate characters recognition.

In this paper, we propose a novel automatic license plate recognition (ALPR) method based on convolutional neural network to achieve a better performance in detecting and recognizing license plate (LP) with relatively large angle of inclination. Most existing methods only perform well on dataset where LPs are presented in almost upright position with little or no tilted angle. While, in practice, the LP images collected by roadside cameras or hand-held image capturing devices can be fairly slanted, which causes great difficulties on recognition tasks. To solve this problem, we design an angle correction module and integrate it into a holistic ALPR model with a spatial transformer network embedded inside. The whole model can be trained end-to-end by back-propagation. A large and comprehensive rotated LP dataset Rlpd is collected and introduced in our work for model training and testing. Through extensive experiments, this approach is proved to have a better performance on tilted license plate dataset in terms of accuracy and computational cost than other state-of-the-art methods.

Automatic Recognition for Arbitrarily Tilted License Plate

We propose an advanced Automatic number-plate recognition (ANPR) system, which not only recognizes the number and the issuing state, but also the type and location of the vehicle in the input image. The system is based on a combination of existing methods, modifications to neural network architectures and improvements in the training process. The proposed system uses machine-learning approach and consists of three main parts: segmentation of input image by Fully Convolutional Network for localization of license plate and determination of vehicle type; recognition of the characters of the localized plate by a Maxout CNN and LSTM; determination of the state that has issued the license plate by a CNN. The training of these neural network models is accomplished using a manually labeled custom dataset, which is expanded with data augmented techniques. The resulting system is capable of localizing and classifying multiple types of vehicles (including motorcycles and emergency vehicles) as well as their license plates. The achieved precision of the localization is 99.5%. The whole number recognition accuracy is 96.7% and character level recognition accuracy is 98.8%. The determination of issuing state is precise in 92.8% cases.

FCN and LSTM Based Computer Vision System for Recognition of Vehicle Type, License Plate Number, and Registration Country

Problem of transferring and testing self-driving algorithms developed in virtual environment to a physical environment is explored by transferring a Convolutional Neural Network based self-driving car steering algorithm from virtual environment to physical RC card based environment for validation and testing as a step on the way for full scale selfdriving car tests. In the process a novel approach for synthetic training data generation from single camera is developed, thus reducing the real world physical requirements for the algorithm and demonstrating the improved self-driving algorithm development pipeline from fully virtual environments to scaled physical models, to full self-driving cars, potentially leveraging the global developer community for development.

/pdf/prospects-of-improving-the-self-driving-car-development-pvk7od9tpn.pdf

Nauris Dorbe

Papers

FCN and LSTM Based Computer Vision System for Recognition of Vehicle Type, License Plate Number, and Registration Country

Prospects of Improving the self-Driving Car Development Pipeline: Transfer of Algorithms from Virtual to Physical Environment