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Recently, in the field of speech processing, I-Vector modeling has been appealed a great deal of interest. I-Vector has shown its benefits in modeling of intra and inter-domain variabilities to a single low dimension space for speaker identification tasks. This paper presents the usage of I-Vector in camera identification as a new approach in image forensics domain. In our approach, image texture...
Recent advances have enabled oracle classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much...
Person re-identification is an open and challenging problem in computer vision. Existing approaches have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where a new camera may...
In this work, we decompose a first-person action into verb and noun. We then study how the coupling of an action's constituent verb and noun affects the learners' ability to learn them separately and to combine them to perform recognition. We compare different information fusion methods on conventional action recognition and zero-shot learning, of which the latter is a strong indication of the feature's...
Despite being an essential prerequisite at the basis of many applications ranging from surveillance to computational photography, the problem of initial background estimation seems to be marginally investigated. In this paper, we present a reliable CNN-based solution to estimate the initial background (BG) of a scene, given not necessarily a whole sequence but just a small set of frames containing...
Human detection is an essential task in so many applications, especially surveillance systems. Recently, ConvNets (Convolutional Neural Networks)-based YOLO model is a successful method applied for object (including human) detection. It is one of the fastest way to detect directly objects from the input image. However, compared to the ConvNets-based state-of-the-art object detection methods, YOLO...
Domain adaptation (DA) algorithms address the problem of distribution shift between training and testing data. Recent approaches transform data into a shared subspace by minimizing the shift between their marginal distributions. We propose a method to learn a common subspace that will leverage the class conditional distributions of training samples along with reducing the marginal distribution shift...
Omnidirectional cameras are commonly used in computer vision and robotics. Their main advantage is their wide field of view which allows them to acquire a 360 degree view of the scene with only one sensor and a single shot. However, few studies have investigated the human detection problem using this kind of cameras. In this paper, we propose to extend the conventional approach for human detection...
The task of matching persons across non-overlapping camera views, known as person re-identification, is rather challenging due to strong visual similarity and large appearance changes caused by illumination, pose and occlusion. Most approaches rely on low-level features that are both discriminative and invariant. In this work, we propose a novel method to address this problem by fusing mid-level semantic...
For cross-view action recognition and many real-world visual classification problems, one needs to recognize test data at a particular target domain of interest, while training data are collected at a different source domain. Without eliminating such domain differences, recognition of test data using classifiers trained in the source domain will not be expected to produce satisfactory performance...
In this paper, we propose a sparse coding approach to background modeling. The obtained model is based on dictionaries which we learn and keep up to date as new data are provided by a video camera. We observe that, without dynamic events, video frames may be seen as noisy data belonging to the background. Over time, such background is subject to local and global changes due to variable illumination...
Background subtraction is important for many vision applications. Existing techniques can adapt to gradual changes in illumination but fail to cope with sudden changes often seen in indoor environment. In this paper, we propose a novel background subtraction technique that models the change of illumination as a regression function of spatial image coordinates. Such spatial dependency is significant...
In this paper, a selective eigenbackgrounds method is proposed for background subtraction in crowded scenes. In order to train and update the eigenbackground model with frames containing few objects (i.e. clean frames), virtual frames are constructed based on a frame selection map. Then, the eigenbackground that best depicts background is selected for each pixel based on an eigenbackground selection...
This paper addresses the problem of UGV navigation in various environments and lightning conditions. Previous approaches use a combination of different sensors, or work well, only in scenarios with noticeable road marking or borders. Our robot is used for chemical, nuclear and biological contamination measurement. Thus, to avoid complications with decontamination, only a monocular camera serves as...
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