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Human faces undergo considerable amount of variations across ages. This paper proposes an age-invariant face verification method by using a Local Classifier Ensemble Model (LCEM). First, reference points are located based on an extended Active Shape Model and faces are aligned afterwards. Second, a face is grouped into several non-overlapping patches and each group is further divided into several...
In this paper, we propose an approach for human activity categorizing based on the use of optical flow direction and magnitude features. The main contribution of this paper is the feature representation that mirrors the geometry of the human body and relationships between its moving regions when performing activities. The features are quantified using a quantization algorithm. We analyze the performance...
We may represent human actions as a bag of spatiotemporal visual words extracted from input video sequences. For human action categorization, labeled LDA (L-LDA) is an extension of latent Dirichlet allocation (LDA) by providing action class labels to each video. To handle parameter uncertainty in L-LDA, this paper further extends L-LDA within the type-2 fuzzy set (T2 FS) framework, referred to as...
In traditional bag-of-words method, each local feature is treated evenly for representation. One disadvantage of this method is that it is not robust to noise, which makes the performance impaired. In this paper, a novel human action recognition approach which learns weights for features is proposed, where each feature is assigned a weight for human action representation. These weights are learned...
The aim of this paper is to track objects during their use by humans. The task is difficult because these objects are small, fast-moving and often occluded by the user. We present a novel solution based on cascade action recognition, a learned mapping between body-and object-poses, and a hierarchical extension of importance sampling. During tracking, body pose estimates from a Kinect sensor are classified...
This paper tackles a challenging problem of inertial sensor-based recognition for similar walking action classes. We solve two remaining problems of existing methods in the case of walking actions: action signal segmentation and recognition of similar action classes. First, to robustly segment the walking action under drastic changes such as speed, intensity, or style, we rely on the likelihood of...
Automatic 3D reconstruction of human poses from monocular images is a challenging and popular topic in the computer vision community, which provides a wide range of applications in multiple areas. Solutions for 3D pose estimation involve various learning approaches, such as support vector machines and Gaussian processes, but many encounter difficulties in cluttered scenarios and require additional...
A ceiling sensor system is reported in this study to recognize different activities of multiple persons in the home environment. The sensors output binary sequences by which we know the existence/nonexistence of persons under the sensors. A short-period average of the binary response is shown to be regarded as a pixel value of a top view camera, but the camera-like view is more advantage in the sense...
Despite their high stability and compactness, affine moment invariants have received a relatively little attention in action recognition literature. In this paper, we introduce an approach for activity recognition, based on affine moment invariants. In the proposed approach, a compact computationally-efficient shape descriptor is developed by using affine moment invariants. Affine moment invariants...
In this paper, effective grading of arecanuts is proposed. The Arecanut RGB image is converted into YCBCR color space. Three sigma control limits on color features are determined for effective segmentation of arecanuts. Color features are used for the grading of arecants with the help of support vector machines (SVMs) into two grades i.e. Boiling and Non-boiling nuts effectively. Experimental k-fold...
Human activity recognition is widely researched in the various filed these days. For the aged care, the one of the most important activities of old people is fall, since it causes often serious physical and psychological results. Many researchers have studied human activity recognition techniques in various domains; however none released to a commercial product satisfying the old people requirements,...
In retail stores, cashier non-compliance activities at the Point of Sale (POS) are one of the prevalent sources of retail loss. In this paper, we propose a novel approach to reliably rank the list of detected non-compliance activities of a given retail surveillance system, thereby provide a means of significantly reducing the false alarms and improving the precision in non-compliance detection. Our...
Blogs have become an important medium for people to publish their opinions and ideas on the Web. However, it is still not clear whether we can analyze political public opinions from blogs. There have been some recent work on political viewpoint classification, but most only classified political blog entries or sites into opposing viewpoints such as conservative/liberal or Israeli/Palestinian. However,...
We investigate pedestrian detection in depth images. Unlike pedestrian detection in intensity images, pedestrian detection in depth images can reduce the effect of complex background and illumination variation. We propose a new feature descriptor, Histogram of Depth Difference(HDD), for this task. The proposed HDD feature descriptor can describe the depth variance in a local region as Histogram of...
In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the...
We earlier introduced a novel framework for realization of Adaptive Autonomy (AA) in human-automation interaction (HAI). This study presents an expert system for realization of AA, using Support Vector Machine (SVM), referred to as Adaptive Autonomy Support Vector Machine Expert System (AASVMES). The proposed system prescribes proper Levels of Automation (LOAs) for various environmental conditions,...
Previously, most human tracking system mainly focuses on conventional imaging system that depends on the illumination and has a limited field of view. In this paper, we propose to introduce a novel surveillance system that is Thermal Catadioptric Omnidirectional (TCO) Vision System. The proposed system is able to realize the surveillance in all-weather and big field of view conditions. For human tracking...
Gait recognition and analysis is one of the most important biometric methods for medical treatments, virtual reality games and human motion identification. Gait recognition based on wearable MEMS inertial sensors is proposed for medical rehabilitation with Physical Activities Healthcare System (PATHS) in this paper. We use relative wavelet energy as features for support vector machine (SVM) recognition...
Multi-threaded applications are commonplace in today's software landscape. Pushing the boundaries of concurrency and parallelism, programmers are maximizing performance demanded by stakeholders. However, multi-threaded programs are challenging to test and debug. Prone to their own set of unique faults, such as race conditions, testers need to turn to automated validation tools for assistance. This...
in this paper, we propose a new framework in pedestrian detection by combining the HOG and uniform LBP feature on blocks. Contrast experiment result shows that detector using combined features is more powerful than one single feature. To further improve the detection performance, we make a contrast experiment that the HOG-LBP features are calculated at variable-size blocks to find the most efficient...
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