Kernel-Based Hand Tracking
نویسندگان
چکیده
In this work, a new method is proposed for hand tracking based on a density approximation and optimization method. Considering tracking as a classification problem, we train an approxiator to recognize hands from its background. This procedure is done by extracting feature vector of every pixel in the first frame and then building an approximator to construct a virtual optimized surface of pixels for similarity of the frames which belong to the hand of those frames related to the movie. Received a new video frame, approximator is employed to test the pixels and build a surface. In this method, the features we use is color RGB corresponding to the feature space. Conducting simulations, it is demonstrated that hand tracking based on this method result in acceptable and efficient performance. The experimental results agree with the theoretical results. Key word: Hand Tracking, Kernel Density, Approximator.
منابع مشابه
Visual Tracking using Kernel Projected Measurement and Log-Polar Transformation
Visual Servoing is generally contained of control and feature tracking. Study of previous methods shows that no attempt has been made to optimize these two parts together. In kernel based visual servoing method, the main objective is to combine and optimize these two parts together and to make an entire control loop. This main target is accomplished by using Lyapanov theory. A Lyapanov candidat...
متن کاملUsing a Novel Concept of Potential Pixel Energy for Object Tracking
Abstract In this paper, we propose a new method for kernel based object tracking which tracks the complete non rigid object. Definition the union image blob and mapping it to a new representation which we named as potential pixels matrix are the main part of tracking algorithm. The union image blob is constructed by expanding the previous object region based on the histogram feature. The pote...
متن کاملNeural Network Performance Analysis for Real Time Hand Gesture Tracking Based on Hu Moment and Hybrid Features
This paper presents a comparison study between the multilayer perceptron (MLP) and radial basis function (RBF) neural networks with supervised learning and back propagation algorithm to track hand gestures. Both networks have two output classes which are hand and face. Skin is detected by a regional based algorithm in the image, and then networks are applied on video sequences frame by frame in...
متن کاملA Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities
We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity, and acceleration, while the system’s input are the instantaneous spike rates. The system’s state dynamics is defined as a combination of a linear mapping from the previous estimated state and a kernel-based mapping ta...
متن کاملApplying mean shift and motion detection approaches to hand tracking in sign language
Hand gesture recognition is very important to communicate in sign language. In this paper, an effective object tracking and hand gesture recognition method is proposed. This method is combination of two well-known approaches, the mean shift and the motion detection algorithm. The mean shift algorithm can track objects based on the color, then when hand passes the face occlusion happens. Several...
متن کامل