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We present an approach, based on non-negative matrix factorization, for learning to recognize parallel combinations of initially unknown human motion primitives, associated with ambiguous sets of linguistic labels during training. In the training phase, the learner observes a human producing complex motions which are parallel combinations of initially unknown motion primitives. Each time the human...
An automated approach is proposed which can analyze ground reaction force data from bipedal walking robots and humans. The input of the automated analysis is the raw data from force sensors mounted in the feet of a robot. The output is detailed information, such as detected single support, double support, and swing phases, their durations, timings of events like heel strikes, properties of the phase...
Performance of automatic face recognition algorithm has increased considerably over the past decades. However, face recognition under changes in lighting conditions remains a challenging issue for computers. In this paper, we propose a novel face recognition algorithm inspired by information taken from human fixation patterns. We augment a LGBP (Local Gabor Binary Pattern) algorithm - a well-known...
Pedestrian detection is a significant task in driver assistance systems. This paper presents an in-vehicle system for pedestrian detection. To speed up computation, we parallel implemented the state of the art detection algorithm on a NVIDIA GPU. The experiments demonstrate that our implementation can achieve accurate and real time detection.
This paper presents a new feature descriptor for real-time people detection in depth images. The shape cue in depth images can reduce negative impacts of variations of clothing, lighting conditions and the complexity of backgrounds. The proposed Simplified Local Ternary Patterns (SLTP) can take advantage of depth images to describe human body shape with low computational cost. To evaluate the SLTP...
Visible watermarking mechanism often used to claim the copyright of the protected media from the human visual perception. As a result of the visible logo superposes the media content, the visible logo inevitable distorts the content and degrades the readability of digital media. To diminish the visual distortion but preserve the advantages of visible watermarking technique, in this paper, we proposed...
Spatio-Temporal Interest Point (STIP) has been widely used for human action recognition. However, the performance of the STIP based methods are still limited in realistic datasets which often include large variations in illuminations, viewpoints and camera motions. One reason of the low performance is that the STIPs only reflect the local change in videos, which is not enough to obtain stable informative...
The objective of human re-identification is to recognize a specific individual on different locations and to determine whether an individual has already appeared. This is especially in multi-camera networks with non-overlapping fields of view of interest. However, this is still an unsolved computer vision task due to several challenges, e.g. significant changes of appearance of humans as well as different...
The bag-of-words approach with local spatio-temporal features have become a popular video representation for action recognition. Recent methods have typically focused on capturing global and local statistics of features. However, existing approaches ignore relations between the features, particularly space-time arrangement of features, and thus may not be discriminative enough. Therefore, we propose...
Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship between the attribute and object classifiers. In this...
Pedestrian detection is an important part of intelligent transportation systems. In the literature, Histogram of Oriented Gradients (HOG) detector for pedestrian detection is known for its good performance, but there are still some false detections appearing in the cases with flat area or clustered background. To deal with these problems, in this research work we develop a new feature which is based...
Human detection is one of the hard problems in object detection field. There are many challenges like variation in human pose, different clothes, non-uniform illumination, cluttered background and occlusion which make this problem much harder than other object detection problems. Defining good features, which can be robust to this wide range of variations, is still an open issue in this field. To...
Color signatures, histograms and bag of colors are basic and effective strategies for describing the color content of images, for retrieving images by their color appearance or providing color annotation. In some domains, colors assume a specific meaning for users and the color-based classification and retrieval should mirror the initial suggestions given by users in the training set. For instance...
Image co-occurrence has shown great powers on object classification because it captures the characteristic of individual features and spatial relationship between them simultaneously. For example, Co-occurrence Histogram of Oriented Gradients (CoHOG) has achieved great success on human detection task. However, the gradient orientation in CoHOG is sensitive to noise. In addition, CoHOG does not take...
Kinect, as a 3D digital capturing device, can collect the RGB and depth information of human activities rapidly. We study fusing the depth and RGB information for activity recognition. We introduce histogram color-based image thresholding to detect skin on human body, and use a GMM model to segment human hand areas. We design a new local descriptor, called a 3D Motion Scale-Invariant Feature Transform...
In this paper, we propose a novel human detection approach combining wavelet-based center symmetric LBP (WCS-LBP) with a cascade of random forests. To detect human regions, we first extract three types of WCS-LBP features from a scanning window of wavelet transformed sub-images to reduce the feature dimension. Then, the extracted WCS-LBP descriptors are applied to a cascade of random forests, which...
The paper describes a new method of detecting human figures in the video scene in real time. This problem can be found, for example, in the protection of buildings where unauthorized persons have access, surveillance of persons in common areas such as shopping centers, airport lounges, etc. For the detection of the contour of a human figure the HOG algorithm is often used which detects the human figure...
Human action classification is an important task in computer vision. The Bag-of-Words model uses spatio-temporal features assigned to visual words of a vocabulary and some classification algorithm to attain this goal. In this work we have studied the effect of reducing the vocabulary size using a video word ranking method. We have applied this method to the KTH dataset to obtain a vocabulary with...
The question of scene information whether can help realistic action recognition has been investigated in this paper. The salience region of each frame in video was acquired by using Itti-Koch algorithm. The information outside the salience region represented scene information. Two action recognition methods were tested on the YouTube action dataset. One method got rid of partial scene information,...
Computer vision is a field that includes methods for acquiring, processing, analyzing and understanding images. In the embedded world, computer vision applications have to fight with limited processing power and limited resources to achieve optimized algorithms and high performance. This paper presents work on implementing a human tracking system on both Intel based PC platform and embedded systems...
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