Browsing by Subject "Motion detection"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item Analysis of independent motion detection in 3D scenes(2012-08) Floren, Andrew William; Bovik, Alan C. (Alan Conrad), 1958-In this thesis, we develop an algorithm for detecting independent motion in real-time from 2D image sequences of arbitrarily complex 3D scenes. We discuss the necessary background information in image formation, optical flow, multiple view geometry, robust estimation, and real-time camera and scene pose estimation for constructing and understanding the operation of our algorithm. Furthermore, we provide an overview of existing independent motion detection techniques and compare them to our proposed solution. Unfortunately, the existing independent motion detection techniques were not evaluated quantitatively nor were their source code made publicly available. Therefore, it is not possible to make direct comparisons. Instead, we constructed several comparison algorithms which should have comparable performance to these previous approaches. We developed methods for quantitatively comparing independent motion detection algorithms and found that our solution had the best performance. By establishing a method for quantitatively evaluating these algorithms and publishing our results, we hope to foster better research in this area and help future investigators more quickly advance the state of the art.Item Human extremity detection and its applications in action detection and recognition(2009-12) Yu, Qingfeng; Aggarwal, J. K. (Jagdishkumar Keshoram), 1936-It is proven that locations of internal body joints are sufficient visual cues to characterize human motion. In this dissertation I propose that locations of human extremities including heads, hands and feet provide powerful approximation to internal body motion. I propose detection of precise extremities from contours obtained from image segmentation or contour tracking. Junctions of medial axis of contours are selected as stars. Contour points with a local maximum distance to various stars are chosen as candidate extremities. All the candidates are filtered by cues including proximity to other candidates, visibility to stars and robustness to noise smoothing parameters. I present my applications of using precise extremities for fast human action detection and recognition. Environment specific features are built from precise extremities and feed into a block based Hidden Markov Model to decode the fence climbing action from continuous videos. Precise extremities are grouped into stable contacts if the same extremity does not move for a certain duration. Such stable contacts are utilized to decompose a long continuous video into shorter pieces. Each piece is associated with certain motion features to form primitive motion units. In this way the sequence is abstracted into more meaningful segments and a searching strategy is used to detect the fence climbing action. Moreover, I propose the histogram of extremities as a general posture descriptor. It is tested in a Hidden Markov Model based framework for action recognition. I further propose detection of probable extremities from raw images without any segmentation. Modeling the extremity as an image patch instead of a single point on the contour helps overcome the segmentation difficulty and increase the detection robustness. I represent the extremity patches with Histograms of Oriented Gradients. The detection is achieved by window based image scanning. In order to reduce computation load, I adopt the integral histograms technique without sacrificing accuracy. The result is a probability map where each pixel denotes probability of the patch forming the specific class of extremities. With a probable extremity map, I propose the histogram of probable extremities as another general posture descriptor. It is tested on several data sets and the results are compared with that of precise extremities to show the superiority of probable extremities.Item Mathematical models of motion detection in the fly's visual cortex(Texas Tech University, 2005-12) Chen, Baili; Martin, Clyde F.; Dayawansa, Wijesuriya P.; Sun, ShanVisual motion detection is one of the most active areas in neuroscience today. In this work, we study the mechanism of motion detection in the fly's visual cortex. The work consists of three parts: First, investigate how the direction signals of the moving objects are encoded in the visual cortex of a fly. Several differential equations are derived to model the dendrites which carry information to the tangential cells in the visual cortex of a fly and to model the dynamics in the synaptic inputs. One of these equations can be reduced to an asymmetric forced van-der-pol equation. Studying this equation in detail, it is found that when the parameters are within certain range, there exists a periodic solution. By tracing the trajectory of this solution together with solving the other differential equations, a conclusion is drawn which can explain how the visual system of the fly encodes the motion signal like the change of the direction. The second part of the work aims to find out the mechanism underlying the ¡°vector addition¡± as stated in ¡°population vector¡± hypothesis. Mathematical models are built to model a decending neuron and two tangential cells. Partial differential equations are derived and solved to find out the relation between the input and output of the decending neuron. We come to the conclusion that if the visual cortex of the fly does perform vector addition, this ability should be mainly attributed to the special arrangement of the synaptic locations on the dendrites. In the third part of the work, we propose a hypothesis about how the brain of a fly reconstruct motion trajectory based on the firing rates of the neurons in the brain.Item Mathematical models of motion detection in the fly's visual cortex(2005-12) Chen, Baili; Martin, Clyde F.; Wijesuriya, Dayawansa; Sun, ShanVisual motion detection is one of the most active areas in neuroscience today. In this work, we study the mechanism of motion detection in the fly's visual cortex. The work consists of three parts: First, investigate how the direction signals of the moving objects are encoded in the visual cortex of a fly. Several differential equations are derived to model the dendrites which carry information to the tangential cells in the visual cortex of a fly and to model the dynamics in the synaptic inputs. One of these equations can be reduced to an asymmetric forced van-der-pol equation. Studying this equation in detail, it is found that when the parameters are within certain range, there exists a periodic solution. By tracing the trajectory of this solution together with solving the other differential equations, a conclusion is drawn which can explain how the visual system of the fly encodes the motion signal like the change of the direction. The second part of the work aims to find out the mechanism underlying the ¡°vector addition¡± as stated in ¡°population vector¡± hypothesis. Mathematical models are built to model a decending neuron and two tangential cells. Partial differential equations are derived and solved to find out the relation between the input and output of the decending neuron. We come to the conclusion that if the visual cortex of the fly does perform vector addition, this ability should be mainly attributed to the special arrangement of the synaptic locations on the dendrites. In the third part of the work, we propose a hypothesis about how the brain of a fly reconstruct motion trajectory based on the firing rates of the neurons in the brain.