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Classification of human actions is very challenging and important in many video-based applications. Two common features, i.e., the hand-crafted and the deep-learned ones are usually adopted for video representation and have been proven to be effective in many famous datasets in the literature. However, the hand-crafted feature lacks the ability to detect the discriminative and semantic features and...
Person re-identification is important and challenging parts in a non-overlapping camera network. In this paper, we propose the person re-identification framework which consists of kernel size into convolutional layers considering the person ratio and relationship matrix that train the relationship information related to neighborhoods. Our framework deals with global feature extracted from the whole...
Monocular visual odometry algorithm has been widely used to estimate the pose of aerial robots in GPS denied environments. However, the pure visual system usually has poor robustness in large scale environments. This paper presents a pose estimation algorithm which fuses monocular visual and inertial data using the monocular ORB-SLAM algorithm as the visual framework. Firstly, the scale estimation...
Road markings are important information of transport systems for drivers or intelligent vehicle. Efficient road markings feature extraction is pre-requisite to road markings detection, recognition and visual localization. However, most of previous lane markings feature extractors are operating on conventional images, the feature extraction methods for omnidirectional images are rarely considered in...
Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction. In this work, we present a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for video-based person re-identification, which enables the feature extractor to be aware of the current input video sequences, in a way that interdependency from the matching...
In action recognition, local motion descriptors contribute to effectively representing video sequences where target actions appear in localized spatio-temporal regions. For robust recognition, those fundamental descriptors are required to be invariant against horizontal (mirror) flipping in video frames which frequently occurs due to changes of camera viewpoints and action directions, deteriorating...
Detecting logo frequency and duration in sports videos provides sponsors an effective way to evaluate their advertising efforts. However, general-purposed object detection methods cannot address all the challenges in sports videos. In this paper, we propose a mutual-enhanced approach that can improve the detection of a logo through the information obtained from other simultaneously occurred logos...
In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras. We propose a simple yet effective human part-aligned representation for handling the body part misalignment problem. Our approach decomposes the human body into regions (parts) which are discriminative for person matching, accordingly computes the representations...
Person re-identification (Re-ID) is an important problem in video surveillance, aiming to match pedestrian images across camera views. Currently, most works focus on RGB-based Re-ID. However, in some applications, RGB images are not suitable, e.g. in a dark environment or at night. Infrared (IR) imaging becomes necessary in many visual systems. To that end, matching RGB images with infrared images...
Body Condition Scoring (BCS) is a method of evaluating fatness or thinness in cows, and it is important to manage productivity of the cows. However, it is not easy to measure BCS by observing animals because it consumes much time and costs, especially in the large-scale farming. Therefore, almost farmers are not conducting regular evaluation of BCS. In this paper, we propose the noninvasive method...
Loop closure detection is important in simultaneous localization and mapping (SLAM) systems. In this paper, Generative Adversarial Networks (GAN), an unsupervised deep architecture is employed to detect the loop closure for vision-based SLAM systems. Instead of extracting handcrafted features like SIFT, SURF or ORB. Generative Adversarial Networks are based on image features. Similar to the task about...
A fast method for mobile robot 3D SLAM (simultaneous localization and mapping) is presented to address the problem of 3D modeling in complex indoor environment. According to the camera calibration model and the image feature extraction and matching procedure, the association between two 3D point clouds can be established. On the basis of the RANSAC (random sample consensus) algorithm, the correspondence...
This paper presents a novel strategy addressing visual SLAM with enhancement of data association method. Hyper graph theory and transformation was incorporated within cooperative visual SLAM. The research presented a synthetic approach to fulfill a cooperative data association and fusion strategy for multiple UAVs equipped with stereo vision cameras encountered with indistinct imaging, where conventional...
Vision-based pedestrian detection for all day are crucial in Advance Driver Assistance Systems (ADAS), autonomous vehicles and video surveillance. Based on the fact that human body radiation falls around 9.3pm, thermal images have distinctive advantages in pedestrian detection at nighttime. With the recent success of CNNs in vision community, how to properly explore information in color and thermal...
Visual servoing (VS) is an automatic control technique which uses the feedback of visual information to guide the robot motion to converge to the desired location. This paper proposes new moment-based features for robust VS with spheres, especially in cameras obeying the unified model. Specifically, we introduce a virtual plane to reload contours of image projections of spheres, in the process of...
Triangulation is a fundamental task in 3D computer vision. Unsurprisingly, it is a well-investigated problem with many mature algorithms. However, algorithms for robust triangulation, which are necessary to produce correct results in the presence of egregiously incorrect measurements (i.e., outliers), have received much less attention. The default approach to deal with outliers in triangulation is...
A popular approach to training classifiers of new image classes is to use lower levels of a pre-trained feed-forward neural network and retrain only the top. Thus, most layers simply serve as highly nonlinear feature extractors. While these features were found useful for classifying a variety of scenes and objects, previous work also demonstrated unusual levels of sensitivity to the input especially...
Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn...
Flow pattern is one of the most important parameters for gas-liquid two-phase flow. In this work, a new flow pattern identification method based on Convolution Neural Network (CNN) is presented. A 7-layer CNN structure is chosen, and the parameters of this network are determined by a training set. In order to verify the feasibility, experiments were carried out in horizontal pipe with the inner diameter...
This paper addresses the problem of estimating the alignment pose between two models by using structure-specific local descriptors which are generated by combining 2D image data and 3D contextual shape data. The 2D texture information is represented by a robust SIFT descriptor, and the geometric information is represented by a histogram supported by the orders of curvature and angles between normal...
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