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Currently, robot learning of human activities is mainly studied in two largely disconnected domains: high level semantics understanding in human activity recognition, and low level motion trajectory reproduction in robot imitation learning. The critical problem of human activity unified learning (HAUL) was not well studied in previous work. One important challenge is the lack of a representation that...
This paper discusses the problem of one shot gesture recognition. This is relevant to the field of human-robot interaction, where the user's intentions are indicated through spontaneous gesturing (one shot) to the robot. The novelty of this work consists of learning the process that leads to the creation of a gesture, rather on the gesture itself. In our case, the context involves the way in which...
In deep neural networks, which have been gaining attention in recent years, the features of input images are expressed in a middle layer. Using the information on this feature layer, high performance can be demonstrated in the image recognition field. In the present study, we achieve image recognition, without using convolutional neural networks or sparse coding, through an image feature extraction...
A video contains rich perceptual information, such as visual appearance, motion and audio, which can be used for understanding the activities in videos. Recent works have shown the combination of appearance (spatial) and motion (temporal) clues can significantly improve human action recognition performance in videos. To further explore the multimodal representation of video in action recognition,...
The online writer identification is a required component in many applications of Computer vision and Pattern Recognition. The offline writer identification is more developed in literature due to the use of traditional system based on Image Processing. There is a lack of works done in the case of online writer identification. In this paper, we propose a novel method to text independent writer identification...
The agricultural production video record is an important primitive data in the establishment of agricultural product quality traceability system and digital monitoring system. This paper presents the feature extraction and automatic recognition system of the typical production activities in the agricultural production video record based on the machine learning theory. The system consists of the feature...
The precession feature of space target provides an effective approach for target recognition. However, with the requirements that target detection and tracking to be completed successfully before the processing of precession features extraction can be implemented, the existing methods demand radar resources allocation for target detection, tracking and feature extraction, respectively, thus reducing...
Due to the diversity of body movements and uncertainty of recording occasion, human action recognition is still a challenging task, especially in real world. This paper provides a new method of representing the video with mid-level vision representation which is extracted from the discriminative supervoxels. In the proposed method, the discriminative supervoxels we extracted through a learning phase...
Object detection from still images has been among the most active and challenging area in computer vision recently. In contrast, fully supervised object detection from video has rarely been investigated. In this paper, we propose an algorithm to improve the performance of object detection from video. Our proposed method is based on an empirical property that the trajectory of an object is important...
This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of...
RGB-D action streams have aroused impressive attentions for recognition task, for its geometric characteristic and less influence of illumination. However, there exists large divergences of intra-class actions performed between sub-action, multi-subject and multi-modality, which may affect the result of action recognition. In order to solve these three problems, we propose a Sparse alignment guided...
Aiming at the problem how to express relevant relationship between multiple targets, we propose an approach based on the tracking-by-detection (TBD) strategy, where detections from the HOG classifier are regarded as image evidence. Focusing on the issue of localization uncertainty, data association based on greedy heuristics is executed iteratively to retrieve from the erroneous candidate locations...
Adaptively tracking tram railway in video-based complex scene is difficult because of road curving and environment changing. In this paper, we introduce an adaptive railway recognition method by analyzing gray distribution features of railway region. This method firstly segments track regions using multiple thresholds which can be dynamically optimized based on the change of local accumulation histogram...
Recognizing human action from low-resolution (LR) videos is essential for many applications including large-scale video surveillance, sports video analysis and intelligent aerial vehicles. Currently, state-of-the-art performance in action recognition is achieved by the use of dense trajectories which are extracted by optical flow algorithms. However, the optical flow algorithms are far from perfect...
Nowadays the Doubly-Fed Induction Generator (DFIG) is equipped for many large wind farms. Its faults have a deep influence on the safety and effectiveness of the machine and huge cost is required for maintenance. Investigations have revealed that stator inter-turn short circuit fault may lead to more serious problems. More researches are to focus attention on fault detection and preventive maintenance...
Beyond general object recognition whereby general categories such as dogs and cats are estimated from images, Fine-grained visual categorization (FGVC) is a new trend that goes beyond general object recognition — where general categories such as cats and dogs are estimated from images — to classify fine-grained categories of objects (or animals) such as poodles or bulldogs. It is difficult to distinguish...
The in-air handwriting is a natural and promising humancomputer interaction way. Compared with handwritten Chinese characters on touch screen, the in-air handwritten Chinese characters have their unique characteristics, e.g., each character is always written in a single stroke. In this paper, we propose a high-order directional feature for recognizing in-air handwritten Chinese characters. The proposed...
Automatic robotic drawing is a fantastic demo to show the combination of intelligence and robot technique. It requires automatic feature extraction, complex robot path planning and optimization, which can make many contributions in industrial manufacturing. In this paper, we propose a hybrid method by combining local binarization with global binarization to extract features. The extracted features...
This paper presents a formation control framework based on an integrated position sensor system, including two cameras and several inertial sensors, for Unmanned Aerial Vehicles (UAVs) to fly outdoors. A sliding mode controller provides the desired orientation for the UAV to track a desired trajectory. The flight control system takes into account the estimated attitude and position to guide the follower...
This study proposes a vision based Persian Sign Language (PSL) recognition system. Continuous Hidden Markov Model (HMM) with Gaussian mixture state observation densities is used to classify 15 dynamic signs. The proposed feature extraction approach is based on the spline interpolation of the sign trajectories. The efficiency of the system was assessed with a large set of videos collected by the authors...
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