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Instrumentation able to estimate upper limbs kinematics has a lot of relevance in the context of rehabilitation of stroke patients. The systems based on inertial systems seem to be well suited for clinical environment and numerous authors have presented wearable systems interesting in terms of accuracy and easiness of implementation. These systems always require an initialization procedure and the...
Developing traffic signal control methods is considered as the most important way to improve the traffic efficiency of modern roundabouts. This paper applies a traffic signal controller with two fuzzy layers for signalizing roundabouts. The outer layer of the controller computes urgency degrees of all the phase subsets and then activates the most urgent subset. This mechanism helps to instantly respond...
Human detection based on spectral information is required for various applications, e.g. surveillance, tracking and missing person investigation. In practice, spectral human detection encounters the inherent challenge, i.e. multiple targets detection based on a limited number of spectral bands, because (1) there is a great variety in spectral profiles among various human-related materials, e.g. skin,...
Attributes are visual concepts that can be detected by machines, understood by humans, and shared across categories. They are particularly useful for fine-grained domains where categories are closely related to one other (e.g. bird species recognition). In such scenarios, relevant attributes are often local (e.g. “white belly”), but the question of how to choose these local attributes remains largely...
Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. In this paper, we address such problems by proposing a robust part-based tracking-by-detection framework. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Our approach learns part-based person-specific SVM...
Activity recognition in video is dominated by low- and mid-level features, and while demonstrably capable, by nature, these features carry little semantic meaning. Inspired by the recent object bank approach to image representation, we present Action Bank, a new high-level representation of video. Action bank is comprised of many individual action detectors sampled broadly in semantic space as well...
We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover,...
A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT)[17], Speed-up Robust Feature (SURF)[4], and more recently Binary Robust Invariant Scalable Keypoints (BRISK)[I6] to name a few. These days, the deployment of vision algorithms...
Chinese enterprises have been conducting low-end processing for foreign brands. In recent years, they want to get rid of this high-pay low-income pattern and develop towards the high-end of the value chain. Most of them are transforming to service-focused enterprises that aim to provide customers with customized service. In service-focused enterprises, the human asset ¨C like industry experts, technology...
This paper presents the design and evaluation of a full AI controller for Real-Time Strategy (RTS) games using techniques from Evolutionary Computing (EC). The design is novel in its use of a modified Pareto Differential Evolution (PDE) algorithm for bi-objective optimization of the weights of an Artificial Neural Network (ANN) controller when only single-objective optimization has so far been studied...
We present a hierarchical model for human activity recognition in entire multi-person scenes. Our model describes human behaviour at multiple levels of detail, ranging from low-level actions through to high-level events. We also include a model of social roles, the expected behaviours of certain people, or groups of people, in a scene. The hierarchical model includes these varied representations,...
A general problem for human-machine interaction occurs when a machine's controllable dimensions outnumber the control channels available to its human user. In this work, we examine one prominent example of this problem: amputee switching between the multiple functions of a powered artificial limb. We propose a dynamic switching approach that learns during ongoing interaction to anticipate user behaviour,...
In many visual classification tasks the spatial distribution of discriminative information is (i) non uniform e.g. person ‘reading’ can be distinguished from ‘taking a photo’ based on the area around the arms i.e. ignoring the legs and (ii) has intra class variations e.g. different readers may hold the books differently. Motivated by these observations, we propose to learn the discriminative spatial...
Fine-grained categorization refers to the task of classifying objects that belong to the same basic-level class (e.g. different bird species) and share similar shape or visual appearances. Most of the state-of-the-art basic-level object classification algorithms have difficulties in this challenging problem. One reason for this can be attributed to the popular codebook-based image representation,...
The fast radial symmetry (FRS) transform has been very popular for detecting interest points based on local radial symmetry1. Although FRS delivers good performance at a relatively low computational cost and is very well suited for a variety of real-time computer vision applications, it is not invariant to perspective distortions. Moreover, even perfectly (radially) symmetric visual patterns in the...
In the paper the new results of the safety investigations of the multistate complex systems with dependent components in variable operation conditions called critical infrastructures are presented. The multi-state safety function of the critical infrastructure system is defined and determined for an exemplary critical infrastructure. In the developed models, it is assumed that the system components...
We introduce a saliency model based on two key ideas. The first one is considering local and global image patch rarities as two complementary processes. The second one is based on our observation that for different images, one of the RGB and Lab color spaces outperforms the other in saliency detection. We propose a framework that measures patch rarities in each color space and combines them in a final...
Depth ordering is instrumental for understanding the 3D geometry of an image. Humans are surprisingly good at depth ordering even with abstract 2D line drawings. In this paper we propose a learning-based framework for depth ordering inference. Boundary and junction characteristics are important clues for this task, and we have developed new features based on these attributes. Although each feature...
A driver assistance system realizes that the driver is distracted and that a potentially hazardous situation is emerging. Where should it guide the attention of the driver? Optimally to the spot that allows the driver to make the best decision. Pedestrian detectability has been proposed recently as a measure of the probability that a driver perceives pedestrians in an image [9]. Leveraging this information...
Biometry is a wide-used and pervasive approach to verify people identity, based on the presence of some unique characteristics, having precise properties (i.e. biometric characteristics). On the other hand, optical detection of random features is widely used for recognizing object authenticity. In this paper we explain how these two approaches are similar; random features can be seen as "fingerprint"...
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