The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The proof of human parts has an imperative effect on pose evaluation, and can be effortlessly confused with difficult background due to indefinite part detector. This paper circumvents this predicament by performing a proof supporting approach, where each part also receives confidence from its neighborhood which uses the outline information between connect parts and mitigates the risk of being blindly...
The performance of an object detection system relies heavily on two components: an object model to capture the compositional relationship among the object body and its parts, and a feature representation to describe object appearance. In this work, we present an empirical study of combining two state-of-the-art such components: Deformable Part Model (DPM), a proven effective and flexible part-based...
In this paper, we present a method that combines a sparse appearance model into the Bayesian inference framework for tracking pedestrians in video sequences captured by a fixed camera. We formulate sparse appearance model as a linear combination of a set of 4D smoothed colour histograms for each pedestrian. These colour histograms are computed for all detection windows with different confidence values...
In radar detector performance analysis, it is known there exist targets that do not follow commonly used radar cross-section (RCS) fluctuation models such as the Swerling models. With advances in computational power, the full RCS distributions of many targets can be computed using electromagnetic (EM) simulations. However, these simulated RCS distributions are not fully exploited by radar designers...
In this paper, the authors propose an efficient algorithm to detect lanes on an improved feature map based on Inverse Perspective Mapping (IPM). IPM removes the perspective effect from the road image and creates a bird's eye view of the road scene. A conventional approach to estimate the lateral offset of the lanes from the IPM image is by adding feature points column by column. However, when the...
Detecting different categories of objects in an image and video content is one of the fundamental tasks in computer vision research. Pedestrian detection is a hot research topic, with several applications including robotics, surveillance and automotive safety. Pedestrians are key participants in transportation systems, so pedestrian detection in video surveillance systems is of great significance...
There has been an increased interest in the field of abnormal human activity detection to find a good descriptor with a lower computational cost. In this paper, we propose such a Spatio-Temporal Descriptor (STD) based on spatio-temporal features of an image sequence. Proposed descriptor is based on a texture map, known as Spatio-Temporal Texture Map (STTM) and is based on 3-dimensional Harris function...
This paper presents an improved hierarchical bag-of-words model based on local space-time features to generate multi-level features, in which higher-level features are generated by lower-level feature neighborhoods. An improved method is developed to extract low-level local space-time features, in which the concept of video orthogonal planes is introduced, and interest points are detected on video...
A model for detecting contours in natural images is presented by combining the visual perceptual mechanisms and machine learning. The surround stimuli will enhance the response of the central stimulus if they can form a precise spatial configuration. On the other hand, surround inhibition will reduce the responses to homogeneous elements. Facilitation and inhibition activities in the primary visual...
We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the...
In this paper we introduce a new person tracking-by-detection approach based on a particle filter. We leverage detection and appearance cues and apply explicit occlusion reasoning. The approach samples efficiently from a large set of available person part-detectors in order to increase runtime performance while retaining accuracy. The tracking approach is evaluated and compared to the state of the...
In this paper, we propose a new approach for people detection using a relational feature model (RFM) in combination with histogram similarity functions such as the bhattacharyya distance, histogram intersection, histogram correlation and the chi-square histogram similarity function. The relational features are computed for all combinations of extracted features from a feature detection algorithm...
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...
We present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, an extension of the Bag-of-Visual-Words (BoVW) model, that benefits from the multimodal fusion of visual and depth features. State-of-the-art RGB and depth features, including a new proposed depth descriptor, are analysed and combined in a late fusion fashion. The method is integrated in a continuous gesture recognition...
This paper presents a new model for capturing spatial information for object categorization with bag-of-words (BOW). BOW models have recently become popular for the task of object recognition, owing to their good performance and simplicity. Much work has been proposed over the years to improve the BOW model, where the Spatial Pyramid Matching (SPM) technique is the most notable. We propose a new method...
In this paper, we present a hierarchical framework for detecting and localizing object by components. The system is structured with a root detector and several component detectors that are trained to separately find the object and different parts of the object on the first level. On the second level the spatial relations model performs detection by combining the root detector and the component detectors...
This paper presents a novel local image descriptor that is robust to general image deformations. A limitation with traditional image descriptors is that they use a single support region for each interest point. For general image deformations, the amount of deformation for each location varies and is unpredictable such that it is difficult to choose the best scale of the support region. To overcome...
Salient region of the image, which is composed of salient or interest points, is the most informative part of the image. In this paper, a saliency-based bottom-up visual attention computational model motivated by visual physiological experimental results is used to detect salient region and extract salient points of images. Meanwhile, a method to select number of the salient points to be extracted...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.