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Large vocabulary gesture recognition using a training set of limited size is a challenging problem in computer vision. With few examples per gesture class, researchers often employ exemplar-based methods such as Dynamic Time Warping (DTW). This paper makes two contributions in the area of exemplar-based gesture recognition: 1) it introduces Multiple-Pass DTW (MP-DTW), a method in which scores from...
Inspired by Gustave Lebon's idea of crowds as single-minded entities, we present a novel approach to describe the behavior of a crowd as a single entity, based on the global movement of the entire aggregate of people conforming the crowd. The present work significantly differs from existing literature where the behavior of single individuals within the crowd are the building blocks to describe crowd...
We study the problem of identifying vehicle trajectories from the sequences of noisy geospatial-temporal datasets. Nowadays we witness the accumulation of vehicle trajectory datasets in the form of the sequences of GPS points. However, in many cases the sequences of GPS points are sparse and noisy so that identifying the actual trajectories of vehicles is hard. Although there are many advanced map-matching...
The widespread application of camera-based surveillance systems has inspired extensive investigation on human localization. In this paper, we proposed a device-free localization method using panoramic camera and indoor map. The proposed method is able to provide precise location information of human object without any terminal device. After preprocessing the images observed with a panoramic camera,...
Automatic identification of the relevant frames of references (or external task parameters) in programming by demonstration using the task-parameterized Gaussian mixture regression (TP-GMM) is addressed in this paper. While performing a given task, there may be several external task parameters, some of which are relevant to the specific task, while some others are not relevant. Identifying the irrelevant...
Unprecedented volumes of location-based information have been produced as a result of the widespread adoption of social network applications and GPS-enabled devices and sensors. Publication of such location data can provide valuable resources for researchers and government agencies in applications ranging from near real-time population-wide health monitoring to planning for future cities. However,...
We present an approach to learn and generate movements for robot actions from human demonstrations using Dynamical Movement Primitives (DMPs) framework. The human hand movements are recorded by a motion tracker using a Kinect sensor with a color-marker glove. We segment an observed movement into simple motion units which are called as motion primitives. Then, each motion primitive will be encoded...
This work addresses two topics. On one hand we study the problem of general measures for spatio-temporal trajectory similarity based on the evaluation of longitudinal and lateral spatio-temporal distance. On the other hand we employ trajectory similarity measures to general situation classification problems for traffic scenarios, which include the well-known problem of lane assignment and driving...
This paper presents an efficient model for combining automotive trajectory planning with predicted environment interactions, named progressively interacting trajectories (PITRA). It allows to plan trajectories for fully-automated vehicles by actively considering how other traffic participants will react to the trajectory, while retaining many of the advantages of variational trajectory optimization...
Nearly 1.3 million people die each year in traffic-related accidents, whereas an additional 20–50 million people are injured. Introducing autonomous vehicles would aim to reduce these numbers by removing the driver from the loop entirely and thus removing the human error. Intersections are considered a complex traffic situation for autonomous vehicles. Functions which could accurately foresee future...
Behavior analysis of vehicles surrounding the egovehicle is an essential component in safe and pleasant autonomous driving. This study develops a framework for activity classification of observed on-road vehicles using 3D trajectory cues and a Long Short Term Memory (LSTM) model. As a case study, we aim to classify maneuvers of surrounding vehicles at four way intersections. LIDAR, GPS, and IMU measurements...
Group-based recreational activities have shown to have a number of health benefits for people of all ages. The handful of social robots designed to facilitate such activities are currently only able to implement a priori known recreational activities that have been pre-programmed by human experts. Once deployed in their intended facility, these robots are not able to learn new activities from non-expert...
In this paper, a solution to the problem of Active Authentication using trace histories is addressed. Specifically, the task is to perform user verification on mobile devices using historical location traces of the user as a function of time. Considering the movement of a human as a Markovian motion, a modified Hidden Markov Model (HMM)-based solution is proposed. The proposed method, namely the Marginally...
Human activity recognition plays an important role in personal assistive robot, being able to recognize human activity and perform corresponding assistive action is a great challenges for personal assistive robot. Human body is an articulated system of rigid segments that can be divided into five parts, but many existing methods always identify actions based on the motion trajectories of whole body...
Dynamic Movement Primitives (DMPs) are a generic approach for trajectory modeling in an attractor land-scape based on differential dynamical systems. DMPs guarantee stability and convergence properties of learned trajectories, and scale well to high dimensional data. In this paper, we propose DMP+, a modified formulation of DMPs which, while preserving the desirable properties of the original, 1)...
Learning to perform tasks like pulling a door handle or pushing a button, inherently easy for a human, can be surprisingly difficult for a robot. A crucial problem in these kinds of in-contact tasks is the context specificity of pose and force requirements. In this paper, a robot learns in-contact tasks from human kinesthetic demonstrations. To address the need to balance between the position and...
Human motion is fast and hard to predict. To implement a provably safe collision-avoidance strategy for robots in collaborative spaces with humans, an overapproximative prediction of the occupancy of the human is required, which needs to be calculated faster than real time. We present a method for computing volumes containing the entire possible future occupancy of the human, given its state, faster...
Directions into Velocities of Articulators (DIVA) model is a kind of self-adaptive neural network model which controls movements of a simulated vocal tract to produce words, syllables or phonemes. However, DIVA model lacks of emotion functions. To implement the emotion function in DIVA model, we investigate the process of affective speech production based on the combination of fundamental frequency...
Always-on localization is an important problem for a lot of context sensitive mobile computing applications. This paper proposes WaveLoc, which effectively uses measurements from a trajectory as its fingerprint for localization. Different from traditional approaches, which use signatures from single-points for localization, we leverage signatures from a trajectory, since it offers a lot more information...
This paper attempts to recognize online Farsi handwriting using the freeman chain codes and hidden Markov model. Chain codes reduce the number of data with using the direction of breaks and keeping the direction of pen movement. Hence, it can be used as an effective way to recognition of online sub-words. After breaking the sub-word into component parts (main body and strokes), each part separately...
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