In order to study the application effect and function of industrial visual perception technology in smart city, the image processing and quality evaluation system was constructed by using convolutional neural network (CNN) and Internet of things (IoT) technology. The system was simulated, and then the quality performance of image and video obtained by using industrial visual perception technology was

Pedestrian detection in images is a topic that has been studied extensively, but existing detectors designed for perspective images do not perform as successfully on images taken with top-view fisheye cameras, mainly due to the orientation variation of people in such images. In our proposed approach, several perspective views are generated from a fisheye image and then concatenated to form a composite

Vehicle re-identification is an important part of intelligent transportation. Although much work has been done on this subject in recent years, vehicle re-identification is still a challenging task due to its obvious illumination change, high similarity between inter-class and great changes under different views. As discriminatory local areas and vehicle view information is the key to improving the

Human-robot interaction (HRI) is a multidisciplinary area that consists of several technologies that are used to create various smart city applications. The knowledge gain and analysis of the smart city environment improves response time. This paper introduces the dependable information processing (DIP) method for handling multi-attribute environmental information in a smart city application. Information

Deep learning-based object detection solutions emerged from computer vision has captivated full attention in recent years. The growing UAV market trends and interest in potential applications such as surveillance, visual navigation, object detection, and sensors-based obstacle avoidance planning have been holding good promises in the area of deep learning. Object detection algorithms implemented in

With the widespread use of mobile devices, iris recognition systems encounter more challenges, such as the vulnerability of Presentation Attack Detection (PAD). Recent works pointed out the contact lens attacks, especially images captured under the uncontrolled environment, as a hard task for iris PAD. In this paper, we propose a novel framework for detecting iris presentation attacks that especially

Multimodal image fusion aims to generate a fused image from different signals that captured by multimodal sensors. Although the images obtained by multimodal sensors have different appearances, the information included in these images might be redundant and noisy. In the previous studies, the fusion rule and their properties that guiding how to merge the features from multiple images is relatively

Vehicle model recognition (VMR) is a typical fine-grained classification task in computer vision. To improve the representation power of classical CNN networks for this special task, we focus on enhancing the subtle difference of features and their spatial encoding based on the attention mechanism, and then propose a novel architectural unit, which we term the “convolutional attention model” (CAM)

The limited capacity to recognize micro-expressions with subtle and rapid motion changes is a long-standing problem that presents a unique challenge for expression recognition systems and even for humans. The problem regarding micro-expression is less covered by research when compared to macro-expression. Nevertheless, micro-expression recognition (MER) is imperative to exploit the full potential of

In this paper, we propose an infrared and visible image fusion framework based on the consensus problem. Most current infrared and visible image fusion models aim to transfer only one characteristic of each source domain to the final fusion result. This mechanism limits the performances of fusion algorithms under different conditions. We present a general fusion framework based to solve the global

Recent advances in deep learning have shown excellent performance in various scene understanding tasks. However, in some complex environments or under challenging conditions, it is necessary to employ multiple modalities that provide complementary information on the same scene. A variety of studies have demonstrated that deep multimodal fusion for semantic image segmentation achieves significant performance

The application of computer vision (CV) in healthcare applications is familiar with the wireless and communication technology. CV methods are incorporated in the healthcare for providing programmed interactions towards patient monitoring. The requirements of systems are the analysis and detection of the images' visualization of patients. In this paper, a multi-modal visualization analysis (MMVA) method

Today, with the rapid development of science and technology, the era of big data has been proposed and triggered reforms in all walks of life. Face recognition is a biometric recognition method with the characteristics of non-contact, non mandatory, friendly and harmonious, which has a good application prospect in the fields of national security and social security. With the deepening of the research

Although many studies have investigated markerless motion capture, the technology has not been applied to real sports or concerts. In this paper, we propose a markerless motion capture method with spatiotemporal accuracy and smoothness from multiple cameras in wide-space and multi-person environments. The proposed method predicts each person's 3D pose and determines the bounding box of multi-camera

Crowd counting is an important surveillance application and receives significant attention from the computer vision community. Most of the current methods treat crowd counting by density map estimation and use the Fully Convolution Network (FCN) for prediction. The mainstream framework is to predict density maps and use the sum up the density maps to get the number of people. In such methods, the main

The detection of objects with multi-orientations and multi-scales in aerial images is receiving increasing attention because of numerous useful applications in computer vision, image understanding, satellite remote sensing and surveillance. However, such detection can be exceedingly challenging because of a birds eye view, multi-scale rotating objects with large aspect ratios, dense distributions and

Nowadays, the technology of using computer vision to calibrate objects is widely used, which has a huge market demand in many fields. This paper provides a calibration method of computer vision system based on dual attention neural network. This paper uses the camera to simulate human eyes to obtain three-dimensional images. After obtaining the three-dimensional images, the images are input into the

Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network

Facial expression recognition aims to accurately interpret facial muscle movements in affective states (emotions). Previous studies have proposed holistic analysis of the face, as well as the extraction of features pertained only to specific facial regions towards expression recognition. While classically the latter have shown better performances, we here explore this in the context of deep learning

Periocular region is considered as an important biometric trait due to its ease of collectability and high acceptability in our society. Recent advancements in surveillance applications require infra-red (IR) sensing equipments to be deployed in order to capture the activities occurring in low-light conditions. This gives rise to the problem of matching periocular images in heterogeneous environments

Traditionally, the P3P problem is solved by firstly transforming its 3 quadratic equations into a quartic one, then by locating the roots of the resulting quartic equation and verifying whether a root does really correspond to a true solution of the P3P problem itself. It is well known that a root of the quartic equation could correspond to 2, or 1, or even null solution at all to the P3P problem,

Surveillance camera networks are a useful infrastructure for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network. Most multi-camera tracking works focus on target re-identification and trajectory association problems to track the target. However, since camera networks can generate enormous amount of video data

Building extraction, which is a fundamental task in the community of remote sensing image analysis, has been extensively applied in various applications related to smart cities. Due to the complicated background information in urban areas, how to extract building footprints from high-resolution aerial images is challenging. The recent achievements of deep learning have shed light on building extraction

In biometrics, periocular recognition analysis is an essential constituent for identifying the human being. Among prevailing the modalities, ocular biometric traits such as iris, sclera and periocular eye movement have experienced noteworthy consciousness in the recent past. In this paper, we are presenting new multi-biometric fusion method called Phase Intensive Mutual Exclusive Distribution (PI-MED)

Generative adversarial networks (GANs) have been enjoying considerable success in recent years. However, mode collapse remains a major unsolved problem in training GANs and is one of the main obstacles hindering progress. In this paper, we present spectral regularization for GANs (SR-GANs), a new and robust method for combating the mode collapse problem in GANs. We first perform theoretical analysis

Biometric feature based human authentication systems provide various advantages over the classical authentication systems. Out of numerous biometric features in a human body, the fingerprint is the most commonly used biometric feature for the authentication of a person. Typically, the fingerprint-based systems rely on minutiae points information and use it directly as a user template. Several studies

We propose a novel framework for cross-modal zero-shot learning (ZSL) in the context of sketch-based image retrieval (SBIR). Conventionally, the SBIR schema mainly considers simultaneous mappings among the two image views and the semantic side information. Therefore, it is desirable to consider fine-grained classes mainly in the sketch domain using highly discriminative and semantically rich feature

Augmented and virtual reality deployment is finding increasing use in novel applications. Some of these emerging and foreseen applications allow the users to access sensitive information and functionalities. Head Mounted Displays (HMD) are used to enable such applications and they typically include eye facing cameras to facilitate advanced user interaction. Such integrated cameras capture iris and

A few-shot learning model generally consists of a feature extraction network and a classification module. In this paper, we propose an approach to improve few-shot image classification performance by increasing the representational capacity of the feature extraction network and improving the quality of the features extracted by it. The ability of the feature extraction network to extract highly discriminative

Existing efforts of saliency detection have achieved excellent performance in RGB images, thus to sufficiently exploit existing RGB saliency models and further do some extensions on them, we can transfer existing RGB saliency models to the similar research field, i.e. RGBD saliency detection, by introducing depth cues. Here, we construct a novel RGBD saliency model upon an existing RGB saliency model

The unsupervised person re-identification (re-ID) has become increasingly significant in the community because it is more scalable than the supervised method when dealing with the large-scale person re-ID. However, it is difficult to learn discriminative enough features from across-camera images without labelling information. To address this problem, we propose a joint patch and instance discrimination

There has been a continued interest in learning features that are generalizable across sensors and spectra for ocular biometrics. This is usually facilitated through a model that can learn features that are robust across pose, lighting conditions, spectra, and device sensor variations. In this paper, we propose an efficient deep learning-based feature extraction pipeline for learning the aforementioned

Pyramid Convolutional Attention Network is proposed to efficiently capture long-range dependency and fuse features from different levels for benefitting semantic segmentation problems. In this paper, we focus on how to extract more representative features for segmentation object recognition and design a decoder to recover details in a more efficient way. Inspired by atrous sampling and attention mechanism

Most existing approaches prune wrong matches via estimating an image transformation or solving a graph-based global matching optimization problem, which usually suffers from varying local transformations and outliers. Inspired by the insight that neighboring true matches usually hold consistent local topological structures across images, in this paper we propose a new approach to evaluate the confidence

Current Visual Question Answering (VQA) systems can answer intelligent questions about ‘known’ visual content. However, their performance drops significantly when questions about visually and linguistically ‘unknown’ concepts are presented during inference (‘Open-world’ scenario). A practical VQA system should be able to deal with novel concepts in real world settings. To address this problem, we propose

As the AI (artificial intelligence) develops, driverless vehicle technology is widely concerned, and the important problem that needs to be solved in driverless technology is the detection of pedestrians in the panoramic vision of the vehicle, so the pedestrian detection technology of panoramic vision is explored based on the deep learning. Recently, anchor-free and one-stage detectors have been introduced

Capturing long-range dependencies has proven effective on video understanding tasks. However, previous works address this problem in a pixel pairs manner, which might be inaccurate since pixel pairs contain too limited semantic information. Besides, considerable computations and parameters will be introduced in those methods. Following the pattern of features aggregation in Graph Convolutional Networks

In order to solve the damage caused by the concrete structure, which leads to the reduction of the life of infrastructure, endangers the safety of pedestrians, and has a serious impact on the social economy, building crack detection model of FCN (Fully Convolutional Networks), R-CNN (Regions with CNN feature) and RFCN (Richer Fully Convolutional Networks) has been proposed based on the convolutional

Online multi-object tracking (MOT) is a fundamental problem in video analysis and multimedia applications. The major challenge in the popular tracking-by-detection framework is knowing how to associate candidate detections results with existing tracklets. In this, we propose a non-local attention association approach and apply it to a unified online MOT framework that integrates the merits of single

The periocular region is used for authentication in the recent days under unconstrained acquisition in biometrics. This work presents two new feature extraction techniques to achieve robust and blur invariant biometric verification using periocular images captured using smartphones - (1) Deep Sparse Features (DSF) and (2) Deep Sparse Time Frequency Features (DeSTiFF). Both the approaches are based

The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects are captured from varying distances, under multiple poses, with partial occlusion. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with

Multiple object tracking is an important but challenging computer vision problem. The complex motion of objects makes tracking difficult during long periods of object occlusion, and as a result occlusions frequently cause fragmented tracks with gaps. Previous works use linear interpolation to fill in such gaps, a technique which is only able to model simple motion. As a result, tracked bounding box

An area of biometrics that has recently attracted much attention is gender and age classification. Its applications can be found not only in the fields of security and surveillance, but also in the context of marketing and demographic information gathering. In addition, extracting this information from a biometric sample can help to decrease the time to identify the exact individual. In this paper

Face landmarking, defined as the detection of fiducial points on faces, has received a lot of attention over the last two decades within the computer vision community. While research literature documents major advances using state-of-art deep convolutional neural networks, earlier cascaded regression tree-based approaches remain a relevant alternative for low-cost, low-power embedded systems. Yet,

Evacuation behavior is an important factor which must be considered in the design of public structures. With the continuous complexity of structure, more and more factors should be considered in evacuation. The traditional design based on experience may have some limitations in practice. Based on the deep neural network model, the evacuation design simulation for subway station buildings is implemented

In recent years, Autonomous Driving Systems (ADS) become more and more popular and reliable. Road markings are important for drivers and advanced driver assistance systems by better understanding the road environment. While the detection of road markings may suffer a lot from various illuminations, weather conditions and angles of view, most traditional road marking detection methods use fixed threshold

The correlation between images is crucial for solving the image co-segmentation problem that is segmenting common and salient objects from a set of related images. This paper proposes a novel co-attention computation block to compute the visual correlation between images for improving the co-segmentation performance. Here ‘co-attention’ means that we obtain the co-attention features in encoded features

Facial recognition for surveillance applications still remains challenging in uncontrolled environments, especially with the appearances of masks/veils and different ethnicities effects. Multimodal facial biometrics recognition becomes one of the major studies to overcome such scenarios. However, to cooperate with multimodal facial biometrics, many existing deep learning networks rely on feature concatenation

Domain adaptation has become a resounding success in learning a domain agnostic model that performs well on target dataset by leveraging source dataset which has related data distribution. Most of existing works aim at learning domain-invariant features across different domains, but they ignore the discriminability of learned features although it is import to improve the model's performance. This paper

To study the conservation of architectural heritage, computer algorithms are used to process the artificial field measurements and reference data maps. A 3D virtual model of traditional architecture is constructed for the direct learning and feature extraction from image data through deep learning. A set of tools and processes for pixel-level image processing labeling are proposed, and a label quality

In order to study and predict the freeway traffic safety and realize the traffic flow in the nonlinear big data environment, based on deep learning, the long-short-time memory (LSTM) model based on recurrent neural network is proposed. The traffic flow is predicted and the predicted value of traffic flow is compared with the actual value at different times. The mean absolute percentage error of LSTM

Person Search (PS) has become a major field because of its need in community and in the field of research among researchers. This task aims to find a probe person from whole scene which shows great significance in video surveillance field to track lost people, re-identification, and verification of person. In last few years, deep learning has played unremarkable role for the solution of re-identification

Constructing high-quality panorama is a fundamental task in both computer vision and computer graphics communities, thus, leading to many image stitching approaches for panorama construction. However, panorama constructed by traditional image stitching has a limited angle of view and has an expensively computational cost. To defend the issues, in this paper, we proposed to use video sequence as input

Deep learning-based methods have pushed the limits of the state-of-the-art in face analysis. However, despite their success, these models have raised concerns regarding their bias towards certain demographics. This bias is inflicted both by limited diversity across demographics in the training set, as well as the design of the algorithms. In this work, we investigate the demographic bias of deep learning

In this work, we focus on the Visual Question Answering (VQA) task, where a model must answer a question based on an image, and the VQA-Explanations task, where an explanation is produced to support the answer. We introduce an interpretable model capable of pointing out and consuming information from a novel Knowledge Base (KB) composed of real-world relationships between objects, along with labels

In RGBD saliency detection research field, RGB and depth cues are generally given the same status by RGBD saliency models. However, they ignore that both modalities are significantly different in inherent attribution so that effective features cannot be drawn from depth maps. In order to address this issue, this paper proposes a novel RGBD saliency model including two key components: the contrast-guided

The success of handheld video capturing devices has further fueled the need of improved video stabilization. The videos often contain many foreground facial features like eyes, nose etc. These foreground features can be considered as feature points and may be used to stabilize videos. This paper proposes an innovative and effective digital video stabilization technique, which utilizes foreground features