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The development of automated vehicles brings new challenges to road safety. The behavior of the automated vehicles should be carefully designed in order to interact with the environment and other vehicles efficiently and safely. This paper is focused on the learning and decision making methods for the automated vehicles towards safe freeway driving. Based on a multi-agent traffic model, the decision...
The intention of humans and autonomous robots to interact in shared spatial areas is a challenging field of research regarding human safety, system stability and performance of the system's behavior. In this paper the intention recognition between human and robot from the control point of view are addressed and the time schedule of the exchanged signals is discussed. After a description of the kinematic...
Sign Language Recognition (SLR) aims at translating the sign language into text or speech, so as to realize the communication between deaf-mute people and ordinary people. This paper proposes a framework based on the Hidden Markov Models (HMMs) benefited from the utilization of the trajectories and hand-shape features of the original sign videos, respectively. First, we propose a new trajectory feature...
In this paper, we explore building classifiers to detect Salsa dance step primitives in choreographies available in the Huawei 3DLife data set. These can collectively be an important component of dance tuition systems that support e-learning. A dance step is reasoned as the shortest possible extract of bodily motion that can uniquely identify a particularly repeatable movement through time. The representation...
This study proposes a vision based Persian Sign Language (PSL) recognition system. Continuous Hidden Markov Model (HMM) with Gaussian mixture state observation densities is used to classify 15 dynamic signs. The proposed feature extraction approach is based on the spline interpolation of the sign trajectories. The efficiency of the system was assessed with a large set of videos collected by the authors...
In this work, we study the problem of anomaly detection of the trajectories of objects in a visual scene. For this purpose, we propose a novel representation for trajectories utilizing covariance features. Representing trajectories via co-variance features enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation...
Sign language recognition has been the focus of research in recent years because it has enabled the use of sign languages, which are the main medium of communication for the hearing impaired, for human-computer interaction. In this work, we propose a method to recognize signs using Improved Dense Trajectory (IDT) features which were previously used in large-scale action recognition. Fisher Vectors...
We propose a traffic safety metric called the safety marginal value (SMV) to be applied to discrete-time and continuous-space vehicular traffic networks. Every vehicle uses a set of vehicle states containing the position, velocity, and lane index of all vehicles on a roadway to determine the SMV, while also controlling its velocity for the next time step. The anterior SMV is defined as the minimum...
Abnormal event detection plays an important role in video surveillance and smart camera systems. Existing methods in the literature are usually not object-aware, where different objects are not distinguished in processing. In this work, we propose an efficient object-aware anomaly detection scheme, specifically focusing on certain object categories, such as pedestrians. We first perform a block-based...
We present a system for learning haptic affordance models of complex manipulation skills. The goal of a haptic affordance model is to improve task completion by characterizing the feel of a particular object-action pair. We use learning from demonstration to provide the robot with an example of a successful interaction with a given object. We then use environmental scaffolding and a wrist-mounted...
Automatic recognition of human actions from the videos is of widespread interest for researchers as it has broad range of applications in areas such as video indexing, visual surveillance etc. Several techniques proposed for human action recognition suffer from real-time implications as they are complex and have many tunable parameters. This paper presents a simple and computationally efficient action...
Recently, the satellite-based Vessel Monitoring Systems (VMS) have been widely deployed on fishing vessels. Recognition of fishing activity is the key task for various applications. Previous approaches are basically according to change of vessel's speed; or rely on the validated data from logbooks or documented observations. In this paper, with a rated 60-second temporal resolution VMS data for two...
Recently CCTV-based behavior recognition have gained considerable attention in the transportation surveillance systems to identify normalities, such as traffic jams, accidents, and dangerous driving. An improved method is presented in this paper for the traffic behavior surveillance system by discovering more highly specific features based on the trajectory information. The multiple sparse feature...
As the demands for video surveillance cameras and systems have been greatly increased, more sophisticated techniques for handling video clips from static cameras are required. Trajectory retrieval is one of the relevant applications that exploit such video clips. In this paper, we introduce a system for retrieving object trajectories shown in video clips, captured by static cameras. The system shows...
Tracking and modeling the movement of large number of users in a multi-floor building using wireless devices is a challenging task. This is due to the complexity of crowd movement and the accuracy of signal sensing data. In this paper, we use Layered Hidden Markov Model (LHMM) to fit the spatial-temporal trajectories (with large number of missing values). We decompose the problem into distinct layers...
In the field of gait training with rehabilitation robots, reference robot trajectories that are synchronous to human gait is inevitable. In this paper, a novel adaptive oscillator-based synchronization algorithm using gait events during overground walking is proposed. The gait events are detected with hidden Markov model (HMM), the feature vector of which is measured with wearable inertia measurement...
We propose a novel collision avoidance formulation in the intent space, suitable for navigation of non-holonomic robots in human centered environments. The intent space is characterized by various bands of trajectories wherein each band can be thought to be a representation of a possible human intended motion and the uncertainty associated with it. We ascribe probabilities to human intentions and...
Since digital maps of high spatial and temporal resolution and precision are widely regarded as key enabler for advanced driver assistance systems and automated driving, the research field of automated map generation from crowd-sourced fleet data has gained importance in recent years. This paper proposes a novel optimization method for improving the results of known road-network construction algorithms...
The detect and track of the weak target in Bearing-Time Record (BTR) was studied in this paper. The distribution of the measurements of the BTR was derived. And a method of track before detect algorithm based on Hidden Markov Model was given. The simulation and the experiment with real acoustic data showed the effectiveness of this method. The performance of the two classic algorithms Viterbi and...
State-of-the-art statistical parametric speech synthesis (SPSS) generally uses a vocoder to represent speech signals and parameterize them into features for subsequent modeling. Magnitude spectrum has been a dominant feature over the years. Although perceptual studies have shown that phase spectrum is essential to the quality of synthesized speech, it is often ignored by using a minimum phase filter...
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