Identity recognition in a car cabin is a critical task nowadays and offers a great field of applications ranging from personalizing intelligent cars to suit drivers physical and behavioral needs to increasing safety and security. However, the performance and applicability of published approaches are still not suitable for use in series cars and need to be improved. In this paper, we investigate Human Identity Recognition in a car cabin with Time Series Classification (TSC) and deep neural networks. We use gas and brake pedal pressure as input to our models. This data is easily collectable during driving in everyday situations. Since our classifiers have very little memory requirements and do not require any input data preproccesing, we were able to train on one Intel i5-3210M processor only. Our classification approach is based on a combination of LSTM and ResNet. The network trained on a subset of NUDrive outperforms the ResNet and LSTM models trained solely by 35.9 % and 53.85 % accuracy respectively. We reach a final accuracy of 79.49 % on a 10-drivers subset of NUDrive and 96.90 % on a 5-drivers subset of UTDrive.
North China Electric Power University（华北电力大学）；University of Missouri（密苏里大学）
Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention …
We present a deep recurrent convolutional neural network (CNN) approach to solve the problem of hockey player identification in NHL broadcast videos. Player identification is a difficult computer vision problem mainly because of the players' similar appearance, occlusion, and blurry facial and physical features. However, we can observe players' jersey numbers over time by processing variable length image sequences of players (aka 'tracklets'). We propose an end-to-end trainable ResNet+LSTM network, with a residual network (ResNet) base and a long short-term memory (LSTM) layer, to discover spa…
Aselsan Inc（阿瑟尔桑公司）；Middle East Technical University（中东技术大学）
In this paper, an LSTM autoencoder-based architecture is utilized for drowsiness detection with ResNet-34 as feature extractor. The problem is considered as anomaly detection for a single subject; therefore, only the normal driving representations are learned and it is expected that drowsiness representations, yielding higher reconstruction losses, are to be distinguished according to the knowledge of the network. In our study, the confidence levels of normal and anomaly clips are investigated through the methodology of label assignment such that training performance of LSTM autoencoder and in…
Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence of pixels subject to brightness changes. Differently from classic vision devices, they produce a sparse representation of the scene. Therefore, to apply standard computer vision algorithms, events need to be integrated into a frame or event-surface. This is usually attained through hand-crafted grids that reconstruct the frame using ad-hoc heuristics. In this paper, we propose Matrix-LSTM, a grid of Long Short-Term Memory (LSTM) cells that efficiently process events and learn end-to-end task-dependent event-surfaces. …
Georgia Institute of Technology（佐治亚理工学院）；Georgia Tech Research Institution（佐治亚理工研究所）
Recent two-stream deep Convolutional Neural Networks (ConvNets) have made significant progress in recognizing human actions in videos. Despite their success, methods extending the basic two-stream ConvNet have not systematically explored possible network architectures to further exploit spatiotemporal dynamics within video sequences. Further, such networks often use different baseline two-stream networks. Therefore, the differences and the distinguishing factors between various methods using Recurrent Neural Networks (RNN) or convolutional networks on temporally-constructed feature vectors (Te…
Imperial College London（帝国理工学院）；University of Twente（特文特大学）
This paper presents a classifier ensemble for Facial Expression Recognition (FER) based on models derived from transfer learning. The main experimentation work is conducted for facial action unit detection using feature extraction and fine-tuning convolutional neural networks (CNNs). Several classifiers for extracted CNN codes such as Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs) and Long Short-Term Memory (LSTM) are compared and evaluated. Multi-model ensembles are also used to further improve the performance. We have found that VGG-Face and ResNet are the relatively opti…
Image Captioning, or the automatic generation of descriptions for images, is one of the core problems in Computer Vision and has seen considerable progress using Deep Learning Techniques. We propose to use Inception-ResNet Convolutional Neural Network as encoder to extract features from images, Hierarchical Context based Word Embeddings for word representations and a Deep Stacked Long Short Term Memory network as decoder, in addition to using Image Data Augmentation to avoid over-fitting. For data Augmentation, we use Horizontal and Vertical Flipping in addition to Perspective Transformations …
Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification
Tecnologico de Monterrey（蒙特雷理工学院）；School of Sciences and Engineering（科学与工程学院）；School of Computing, Macquarie University（麦考瑞大学计算学院）；School of Medical and Health Sciences, Tecnologico de Monterrey（蒙特雷理工学院医学与健康科学学院）；University of Victoria（维多利亚大学）
Remote sensing data imputation using deep learning for multispectral imagery
Water Research Centre, University of New South Wales（新南威尔士大学水研究中心）；ARC ITTC Data Analytics for Resources and Environments, University of New South Wales（新南威尔士大学资源与环境数据分析师联盟）；Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, University of New South Wales（新南威尔士大学数学与统计学过渡人工智能研究组）
École de Technologie Supérieure (ÉTS)（高等技术学院）；Université du Québec（魁北克大学）
This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network, namely ResNet-18. Our main motivation for focusing on such a front-end classifier rather than other complex architectures is balancing recognition accuracy and the total number of training parameters. Herein, we measure the impact of different settings required for generating more informative Mel-frequency cepstral coefficient (MFCC), short-time Fourier transform (STFT), an…
Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between differen…
Self-driving vehicles have expanded dramatically over the last few years. Udacity has release a dataset containing, among other data, a set of images with the steering angle captured during driving. The Udacity challenge aimed to predict steering angle based on only the provided images. We explore two different models to perform high quality prediction of steering angles based on images using different deep learning techniques including Transfer Learning, 3D CNN, LSTM and ResNet. If the Udacity challenge was still ongoing, both of our models would have placed in the top ten of all entries.
This work proposes an attention-based sequence-to-sequence model for handwritten word recognition and explores transfer learning for data-efficient training of HTR systems. To overcome training data scarcity, this work leverages models pre-trained on scene text images as a starting point towards tailoring the handwriting recognition models. ResNet feature extraction and bidirectional LSTM-based sequence modeling stages together form an encoder. The prediction stage consists of a decoder and a content-based attention mechanism. The effectiveness of the proposed end-to-end HTR system has been em…
