According to the defects of KNN(K-Nearest Neighbor) algorithm and SVM(Support Vector Machine) algorithm in tracking a moving target such the large consumption and the low accuracy of target tracking error, a tracking model of moving target is proposed based on the combination of KNN algorithm and SVM algorithm with minimum distance optimization. First categories divided according to the princip...

Very recently tracking was approached using classification techniques such as support vector machines. The object to be tracked is discriminated by a classifier from the background. In a similar spirit we propose a novel on-line AdaBoost feature selection algorithm for tracking. The distinct advantage of our method is its capability of on-line training. This allows to adapt the classifier while...

We describe a topic tracking system developed at GE Corporate R&D Center in connection with our participation in DARPA TDT3 evaluations. The TDT tracking task is specified as follows: Given Nt training news stories on a topic, the system must find all subsequent stories on the same topic in all tracked news sources. These sources include radio and television news broadcasts, as well as newswire...

this paper deals with a globally convergent adaptive and sliding mode control of a cart-pole inverted pendulum for trajectory tracking in the presence of a bounded measurement noise and parameter uncertainty. two kinds of controllers have been used for evaluation of tracking error in presence of a bounded noise; as a result, we want to compare that at what time we can see the convergence of tra...

Surveillance missions for multiple autonomous underwater vehicle (AUV) system suggest the use of different modes of operation including organizing and keeping a predefined formation, avoiding obstacles, reaching static and tracking dynamic targets. While exploiting a leader-follower strategy to formation control and the vector Lyapunov function method to controller design, we use discrete-event...

We propose an online visual tracking algorithm by learning discriminative saliency map using Convolutional Neural Network (CNN). Given a CNN pre-trained on a large-scale image repository in offline, our algorithm takes outputs from hidden layers of the network as feature descriptors since they show excellent representation performance in various general visual recognition problems. The features...

Evidence has shown that student’s attention is a crucial factor for engagement and learning gain. Although it can be accurately assessed ad-hoc by an experienced teacher, continuous contact with all students in a large class is difficult to maintain and requires training for novice practitioners. We continue our previous work on investigating unobtrusive measures of body-language in order to pr...

We argue that touch-based gestures on touchscreen devices enable the threat of a form of persistent and ubiquitous tracking which we call touch-based tracking. Touch-based tracking goes beyond the tracking of virtual identities and has the potential for cross-device tracking as well as identifying multiple users using the same device. We demonstrate the likelihood of touchbased tracking by focu...

Computational models of beat tracking of musical audio have been well explored, however, such systems often make “octave errors”, identifying the beat period at double or half the beat rate than that actually recorded in the music. A method is described to detect if octave errors have occurred in beat tracking. Following an initial beat tracking estimation, a feature vector of metrical profile ...

Karyotyping, a standard method for presenting pictures of the human chromosomes for diagnostic purposes, is a long standing, yet common technique in cytogenetics. Automating the chromosome classification process is the first step in designing an automatic karyotyping system. The main aim in this study was to define a new group of features for better representation and classification of chromoso...