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For recognizing human actions in video sequences, it is necessary to extract sufficient information which can represent motion features. In recent years, dense trajectories based action recognition algorithms attract more attention for containing rich spatio-temporal information. However, these algorithms are always faced with cluttered background. To solve this problem, we involve object tracking...
In this paper we proposed an enhanced classification of heuristics and developed a unified heuristic classifying scheme and templates for future researchers to conduct a fair heuristic performance. The process involved a thorough diagnostic of the proposed heuristic classification schemes in order to find unique characteristics that can assist us to distinguish each heuristic. We started by carrying...
In this paper, a possibility of evaluating frame-based nonstationary pattern recognition methods by using Bhattacharrya distance is considered. Speech signal is used as a nonstationary signal and the comparative analysis is done through analyzing the natural speech, isolately spoken Serbian vowels and digits.
In this work, a robust recursive procedure based on WRLS algorithm with VFF and a quadratic classifier with sliding training data set for identification of non-stationary AR model of speech production system is proposed. Experimental analysis is done according to the results obtained in analyzing speech signal with voiced and mixed excitation segments. Presented experimental results justify that two...
Sign recognition has evolved from traditional video-based to 3D-based image recognition. Most documents are presented with Kinect-based somatosensory terminals, which are limited by difficulties in precisely describing the motions performed by various palm joints. The linguistic details of sign language (SL), such as position, direction, and movement, therefore have to be manually inputted. Meanwhile,...
In this paper, we proposed a new trajectory clustering based on partition-cluster-extract (PCE). Firstly, some relative definitions are defined, and then we proposed a new partition method named PCE, which is based on method of partition-group framework. Finally, on the basis of PCE and relative definitions, we proposed a new algorithm named partition-cluster-extract trajectory clustering (PCETC)...
Autonomous steering control is the principal task in the development of an intelligent transportation system. This research paper proposes a novel approach for vision based intelligent control of unmanned vehicles. The paper addresses the problems of accurate and efficient intelligent vehicle control by incorporating a well known evolutionary algorithm cAnt-Miner. The uniqueness of the proposed algorithm...
On the studies of the logistic map, many reports are presented because of the simple structure of the equation. Calculations with finite precision are usually used for implementations of the map for computers. Then a rounding is needed to these calculations. There are five major methods to implement rounding, but there was not any paper on the rounding of the map. In the present paper, we get some...
Motion recognition via trajectory is important in motion analysis for many robotic tasks. A descriptor for motion trajectories plays an important role in the recognition algorithm. Existing invariant descriptors and discrete matching algorithms are not flexible enough under certain circumstances for recognition. In this paper we propose a new descriptor to solve this problem, with a modified data...
In this paper we present a new video object trajectory clustering algorithm, which allows us to model and analyse the patterns of object behaviors based on the extracted features using tensor analysis. The proposed algorithm consists of three steps as follows: extraction of trajectory features by tensor analysis, non-parametric probabilistic mean shift clustering and clustering correction. The performance...
One of the most common operations in exploration and analysis of various kinds of data is clustering, i.e. discovery and interpretation of groups of objects having similar properties and/or behaviors. In clustering, objects are often treated as points in multi-dimensional space of properties. However, structurally complex objects, such as trajectories of moving entities and other kinds of spatio-temporal...
This article presents a method for the calculating similarity of two trajectories. The method is especially designed for a situation where the points of the trajectories are distributed sparsely and at non-equidistant intervals. The proposed method is based on giving different weights to different points: points that are close to each other get smaller weights than the points that do not have neighbors...
This paper describes a method for action recognition using a classification algorithm based on mixtures of von Mises distributions processing action signatures. An action signature is a ID sequence of angles, forming a trajectory, which are extracted from a 2D map of adjusted orientations (subtracting the average orientation) of the gradient of the motion-history image. To obtain the action signature,...
The Self-Splitting Competitive Learning (SSCL) based on the one-prototype-take-one-cluster (OPTOC) learning paradigm is a powerful algorithm that solves the difficult problems of determining the number of clusters and the sensitivity to prototype initialization in clustering. The SSCL algorithm iteratively partitions the data space into natural clusters without a priori information on the number of...
In this paper, we address the pair-activity classification problem, which explores the relationship between two active objects based on their motion information. Our contributions are three-fold. First, we design a set of features, e.g., causality ratio and feedback ratio based on the Granger Causality Test (GCT), for describing the pair-activities encoded as trajectory pairs. These features along...
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