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In this paper, we present an approach for producing side scan sonar image mosaicking under a robust SLAM scheme. A Pose Graph based SLAM algorithm is used to perform a correction over the sensor trajectory for enabling image registration, using observation constraints extracted from the images. However, due to the operational context, the available odometry data carries a high degree of uncertainty...
This paper addresses the heterogeneous data registration problem, which is one of the key features for any scene reconstruction and representation, especially for the underwater environment. In this study, we propose a registration method built around a 2D-to-3D feature-based approach that registers high-resolution side-scan sonar images with bathymetric data (topographic 3D point cloud) obtained...
Reading is one of the main paths to acquire knowledge, either done traditionally on paper media or practiced on electronic devices. Efficiency varies when different reading patterns are involved. It is the objective of this research to classify reading patterns from fixation data using machine learning techniques in an attempt to understand and evaluate the reading and learning process. In our experiment,...
With the growth of crowd phenomena in the real world, crowd scene understanding is becoming an important task in anomaly detection and public security. Visual ambiguities and occlusions, high density, low mobility and scene semantics, however, make this problem a great challenge. In this paper, we propose an end-to-end deep architecture, Convolutional DLSTM (ConvDLSTM), for crowd scene understanding...
A complex activity is a temporal composition of sub-events, and a sub-event typically consists of several low level micro-actions, such as body movement of different actors. Extracting these micro actions explicitly is beneficial for complex activity recognition due to actor selectivity, higher discriminative power, and motion clutter suppression. Moreover, considering both static and motion features...
Human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as medical care, human-computer interaction and virtual training. The task is challenging for feature extraction due to variations in motion performance, recording settings and inter-personal differences. To meet these challenges, we propose two...
Viewpoint variation is a major challenge in video- based human action recognition. We exploit the simultaneous RGB and Depth sensing of RGB-D cameras to address this problem. Our technique capitalizes on the complementary spatio-temporal information in RGB and Depth frames of the RGB-D videos to achieve viewpoint invariant action recognition. We extract view invariant features from the dense trajectories...
One of the promising new directions for Content Based Video Retrieval is object based retrieval which allows the user to manipulate video object as a part of searching and browsing. The major obstacle for the use of objects in video retrieval is the appropriate representation of objects in video database. The purpose of this work is to present an object based framework consisting of entire processing...
The high penetration of Wind Turbine (WT) in the grid is a promising solution to increase the electricity production with renewable energies. In this work, we propose a data-driven methodology for dip voltage fault detection and diagnosis. From experimental measurements the current vector trajectory deformation in the (αβ) reference frame is derived and a statistical-based analysis (first four statistical...
Feature extraction is a key component of a Monocular Simultaneous Localization and Mapping (Monocular SLAM) system which permits to extract features and can also reliably track them over frames. In this paper, a novel approach for Monocular SLAM is proposed. This approach uses the information on the camera displacement and image saliency to adequately extract stable and suitable features, ones that...
In this paper, we propose a CNN-based framework for online MOT. This framework utilizes the merits of single object trackers in adapting appearance models and searching for target in the next frame. Simply applying single object tracker for MOT will encounter the problem in computational efficiency and drifted results caused by occlusion. Our framework achieves computational efficiency by sharing...
Because of the worldwide aging population, more and more elders suffer from dementia. Nowadays, it is inconvenient and time-consuming for doctors to diagnose whether elders who live independently have dementia because lots of diagnostic questions on a checklist must be asked first, and part of them even require a long-term observation. In order to help doctors and make this diagnostic process easier,...
Feature ranking from video-wide temporal evolution brings reliable information for complex action recognition. However, a video may contain similar features in the sequence of frames which deliver unnecessary information to the ranking function. This paper proposes a method to improve the rank-pooling strategy which captures the optimized latent structure of the video sequence data. The optimization...
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...
Action prediction is an important item in the fields of pattem recognition and computer vision. Capturing evolution tendency cues is a key point to action prediction. Dense trajectory (DT) and dynamic image are both effective approaches to explore dynamic information in videos. DT is used to describe the features of action and has achieved state-of-the-art results on action recognition, but often...
The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues over a long period of time in a coherent fashion. In this paper, we present an online method that encodes long-term temporal dependencies across multiple cues. One key challenge of tracking methods is to accurately track occluded targets or those which share similar appearance properties with surrounding...
Modelling and digitizing performing arts through motion capturing interfaces is an important aspect for the analysis, processing and documentation of intangible cultural heritage assets. However, existing modelling approaches may involve huge amounts of information which are difficult to process, store and analyze. To address these limitations, usually a skeleton describing the dancer motion is extracted...
Intelligent Intersection Traffic Management has become increasingly important because of the need to reduce congestion and improve the overall travel experience of commuters. Given the dynamic nature of everyday city traffic, this paper proposes real-time processing of videos from cameras to estimate the traffic density and optimize the signal parameters of the intersection. The region-of-interest...
Recognizing expressions in severely demented Alzheimer's disease (AD) patients is essential, since such patients have lost a substantial amount of their cognitive capacity, and some even their verbal communication ability (e.g., aphasia). This leaves patients dependent on clinical staff to assess their verbal and non-verbal language, in order to communicate important messages, as of the discomfort...
We present a deep trajectory feature representation approach to aid trajectory clustering and motion pattern extraction in videos. The proposed feature representation includes the use of a neural network-based approach that uses the output of the smallest hidden layer of a trained autoencoder to encapsulate trajectory information. The trajectory features are then fed into a mean-shift clustering framework...
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