5,711 research outputs found
Detection of hand gestures with human computer recognition by using support vector machine
Many applications, such as interactive data analysis and sign detection, can benefit from hand gesture recognition. We offer a low-cost approach based on human-computer interaction for predicting hand movements in real time. Our technique involves using a color glove to train a random forest classifier and then predicting a naked hand at the pixel level. Our algorithm anticipates all pixels at a rate of around 3 frames per second and is unaffected by differences in the surroundings. It's also been proven that HCI-based data augmentation is more effective than any other way for enhancing interactive data. In addition, the augmentation experiment was carried out on multiple subsets of the original hand skeleton sequence dataset, each with a different number of classes, as well as on the entire dataset. On practically all subsets, the proposed base architecture improved classification accuracy. When the entire dataset was used, there was even a modest improvement. Correct identification could be regarded as a quality indicator. The best accuracy score was 94.02 percent for the HCI-model with support vector machine (SVM) classifier
New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component
License Plate recognition plays an important role on the traffic monitoring
and parking management systems. In this paper, a fast and real time method has
been proposed which has an appropriate application to find tilt and poor
quality plates. In the proposed method, at the beginning, the image is
converted into binary mode using adaptive threshold. Then, by using some edge
detection and morphology operations, plate number location has been specified.
Finally, if the plat has tilt, its tilt is removed away. This method has been
tested on another paper data set that has different images of the background,
considering distance, and angel of view so that the correct extraction rate of
plate reached at 98.66%.Comment: 3rd IEEE International Conference on Computer and Knowledge
Engineering (ICCKE 2013), October 31 & November 1, 2013, Ferdowsi Universit
Mashha
Controlling a remotely located Robot using Hand Gestures in real time: A DSP implementation
Telepresence is a necessity for present time as we can't reach everywhere and
also it is useful in saving human life at dangerous places. A robot, which
could be controlled from a distant location, can solve these problems. This
could be via communication waves or networking methods. Also controlling should
be in real time and smooth so that it can actuate on every minor signal in an
effective way. This paper discusses a method to control a robot over the
network from a distant location. The robot was controlled by hand gestures
which were captured by the live camera. A DSP board TMS320DM642EVM was used to
implement image pre-processing and fastening the whole system. PCA was used for
gesture classification and robot actuation was done according to predefined
procedures. Classification information was sent over the network in the
experiment. This method is robust and could be used to control any kind of
robot over distance
Simultaneous Localization and Recognition of Dynamic Hand Gestures
A framework for the simultaneous localization and recognition of dynamic hand gestures is proposed. At the core of this framework is a dynamic space-time warping (DSTW) algorithm, that aligns a pair of query and model gestures in both space and time. For every frame of the query sequence, feature detectors generate multiple hand region candidates. Dynamic programming is then used to compute both a global matching cost, which is used to recognize the query gesture, and a warping path, which aligns the query and model sequences in time, and also finds the best hand candidate region in every query frame. The proposed framework includes translation invariant recognition of gestures, a desirable property for many HCI systems. The performance of the approach is evaluated on a dataset of hand signed digits gestured by people wearing short sleeve shirts, in front of a background containing other non-hand skin-colored objects. The algorithm simultaneously localizes the gesturing hand and recognizes the hand-signed digit. Although DSTW is illustrated in a gesture recognition setting, the proposed algorithm is a general method for matching time series, that allows for multiple candidate feature vectors to be extracted at each time step.National Science Foundation (CNS-0202067, IIS-0308213, IIS-0329009); Office of Naval Research (N00014-03-1-0108
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