Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discuss...

Gesture recognition aims at understanding the ongoing human gestures. In this paper, we present a deep architecture to learn spatiotemporal features for gesture recognition. The deep architecture first learns 2D spatiotemporal feature maps using 3D convolutional neural networks (3DCNN) and bidirectional convolutional long-short-term-memory networks (ConvLSTM). The learnt 2D feature maps can encode the global temporal information and local spatial information simultaneously. Then, 2DCNN is utilized further to learn the higher-level spatiotemporal features from the 2D feature maps for the final gesture recognition. The spatiotemporal correlation information is kept through the whole process of feature learning. This makes the deep architecture an effective spatiotemporal feature learner. Experiments on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD) and the Sheffield Kinect Gesture (SKIG) dataset demonstrate the superiority of the proposed deep architecture.

Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

Continuous gesture recognition aims at recognizing the ongoing gestures from continuous gesture sequences, and is more meaningful for the scenarios where the start and end frames of each gesture instance are generally unknown in practical applications. This paper presents an effective deep architecture for continuous gesture recognition. Firstly, continuous gesture sequences are segmented into isolated gesture instances using the proposed temporal dilated Res3D network. A balanced squared hinge loss function is proposed to deal with the imbalance between boundaries and non-boundaries. Temporal dilation can preserve the temporal information for the dense detection of the boundaries at fine granularity, and the large temporal receptive field makes the segmentation results more reasonable and effective. Then, the recognition network is constructed based on the 3D convolutional neural network (3DCNN), the convolutional Long-Short-Term-Memory network (ConvLSTM), and the 2D convolutional neural network (2DCNN) for isolated gesture recognition. The "3DCNN-ConvLSTM-2DCNN" architecture is more effective to learn long-term and deep spatiotemporal features. The proposed segmentation and recognition networks obtain the Jaccard index of 0.7163 on the Chalearn LAP ConGD dataset, which is 0.106 higher than the winner of 2017 ChaLearn LAP Large-scale Continuous Gesture Recognition Challenge.

Convolutional long short-term memory (ConvLSTM) networks have been widely used for action/gesture recognition, and different attention mechanisms have also been embedded into ConvLSTM networks. This paper explores the redundancy of spatial convolutions and the effects of the attention mechanism in ConvLSTM, based on our previous gesture recognition architectures that combine the 3-D convolutional neural network (CNN) and ConvLSTM. Depthwise separable, group, and shuffle convolutions are used to replace the convolutional structures in ConvLSTM for the redundancy analysis. In addition, four ConvLSTM variants are derived for attention analysis: 1) by removing the convolutional structures of the three gates in ConvLSTM; 2) by applying the attention mechanism on the ConvLSTM input; and 3) by reconstructing the input and 4) output gates with the modified channelwise attention mechanism. Evaluation results demonstrate that the spatial convolutions in the three gates scarcely contribute to the spatiotemporal feature fusion and that the attention mechanisms embedded into the input and output gates cannot improve the feature fusion. In other words, ConvLSTM mainly contributes to the temporal fusion along with the recurrent steps to learn long-term spatiotemporal features when taking spatial or spatiotemporal features as input. A new LSTM variant is derived on this basis in which the convolutional structures are embedded only into the input-to-state transition of LSTM. The code of the LSTM variants is publicly available.\footnotehttps://github.com/GuangmingZhu/ConvLSTMForGR.