No description available.

This thesis presents a general framework and method for detection of an object in a video based on apparent motion. The object moves, at some unknown time, differently than the “background” motion, which can be induced from camera motion. The goal of proposed method is to detect and segment the object as soon it moves in an online manner. Since motion estimation can be unreliable between frames, more than two frames are needed to reliably detect the object. Observing more frames before declaring a detection may lead to a more accurate detection and segmentation, since more motion may be observed leading to a stronger motion cue. However, this leads to greater delay. The proposed method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms, defined as declarations of detection before the object moves or incorrect or inaccurate segmentation at the detection time. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.

First generation image coding standards are now well-established and coders based on these standards are commercially available. However, for emerging applications, good quality at even lower bitrates is required. Ways of exploiting higher level visual information are currently being explored by the research community in order to achieve high compression. Unfortunately very high level approaches are bound to be restrictive as they are highly dependent on the accuracy of lower-level vision operations. Region-based coding only relies on mid-level image processing and thus is viewed as a promising strategy. In this work, substantial advances to the field of region-based video compression are made by considering the complete scheme. Thus, improvements to the failure regions coding and the motion compensation components have been devised. The failure region coding component was improved by predicting the texture inside the failure region from the neighbourhood of the region. A significant gain over widely used techniques such as the SA-DCT was obtained. The accuracy of the motion compensation component was increased by keeping an accurate internal representation for each region both at the encoder and the decoder side. The proposed region-based coding system is also evaluated against other systems, including the MPEG4 codec which has been recently approved by the MPEG community.

This paper describes the initial results of a project to create a self-supervised algorithm for learning object segmentation from video data. Developmental psychology and computational experience have demonstrated that the motion segmentation of objects is a simpler, more primitive process than the detection of object boundaries by static image cues. Therefore, motion information provides a plausible supervision signal for learning the static boundary detection task and for evaluating performance on a test set. A video camera and previously developed background subtraction algorithms can automatically produce a large database of motion-segmented images for minimal cost. The purpose of this work is to use the information in such a database to learn how to detect the object boundaries in novel images using static information, such as color, texture, and shape. / Singapore-MIT Alliance (SMA)

The objective of this thesis is to detect and identify moving objects in a video sequence. The currently available techniques for motion estimation can be broadly categorized into two main classes: block matching methods and optical flow methods.<p>This thesis investigates the different motion estimation algorithms used for video processing applications. Among the available motion estimation methods, the Lucas Kanade Optical Flow Algorithm has been used in this thesis for detection of moving objects in a video sequence. Derivatives of image brightness with respect to x-direction, y-direction and time t are calculated to solve the Optical Flow Constraint Equation. The algorithm produces results in the form of horizontal and vertical components of optical flow velocity, u and v respectively. This optical flow velocity is measured in the form of vectors and has been used to segment the moving objects from the video sequence. The algorithm has been applied to different sets of synthetic and real video sequences.<p>This method has been modified to include parameters such as neighborhood size and Gaussian pyramid filtering which improve the motion estimation process. The concept of Gaussian pyramids has been used to simplify the complex video sequences and the optical flow algorithm has been applied to different levels of pyramids. The estimated motion derived from the difference in the optical flow vectors for moving objects and stationary background has been used to segment the moving objects in the video sequences. A combination of erosion and dilation techniques is then used to improve the quality of already segmented content.<p>The Lucas Kanade Optical Flow Algorithm along with other considered parameters produces encouraging motion estimation and segmentation results. The consistency of the algorithm has been tested by the usage of different types of motion and video sequences. Other contributions of this thesis also include a comparative analysis of the optical flow algorithm with other existing motion estimation and segmentation techniques. The comparison shows that there is need to achieve a balance between accuracy and computational speed for the implementation of any motion estimation algorithm in real time for video surveillance.

The objective of this thesis is to detect and identify moving objects in a video sequence. The currently available techniques for motion estimation can be broadly categorized into two main classes: block matching methods and optical flow methods.<p>This thesis investigates the different motion estimation algorithms used for video processing applications. Among the available motion estimation methods, the Lucas Kanade Optical Flow Algorithm has been used in this thesis for detection of moving objects in a video sequence. Derivatives of image brightness with respect to x-direction, y-direction and time t are calculated to solve the Optical Flow Constraint Equation. The algorithm produces results in the form of horizontal and vertical components of optical flow velocity, u and v respectively. This optical flow velocity is measured in the form of vectors and has been used to segment the moving objects from the video sequence. The algorithm has been applied to different sets of synthetic and real video sequences.<p>This method has been modified to include parameters such as neighborhood size and Gaussian pyramid filtering which improve the motion estimation process. The concept of Gaussian pyramids has been used to simplify the complex video sequences and the optical flow algorithm has been applied to different levels of pyramids. The estimated motion derived from the difference in the optical flow vectors for moving objects and stationary background has been used to segment the moving objects in the video sequences. A combination of erosion and dilation techniques is then used to improve the quality of already segmented content.<p>The Lucas Kanade Optical Flow Algorithm along with other considered parameters produces encouraging motion estimation and segmentation results. The consistency of the algorithm has been tested by the usage of different types of motion and video sequences. Other contributions of this thesis also include a comparative analysis of the optical flow algorithm with other existing motion estimation and segmentation techniques. The comparison shows that there is need to achieve a balance between accuracy and computational speed for the implementation of any motion estimation algorithm in real time for video surveillance.

Achieving robot autonomy is an extremely challenging task and it starts with developing algorithms that help the robot understand how humans perceive the environment around them. Once the robot understands how to make sense of its environment, it is easy to make efficient decisions about safe movement. It is hard for robots to perform tasks that come naturally to humans like understanding signboards, classifying traffic lights, planning path around dynamic obstacles, etc. In this work, we take up one such challenge of motion segmentation using Light Detection and Ranging (LiDAR) point clouds. Motion segmentation is the task of classifying a point as either moving or static. As the ego-vehicle moves along the road, it needs to detect moving cars with very high certainty as they are the areas of interest which provide cues to the ego-vehicle to plan it's motion. Motion segmentation algorithms segregate moving cars from static cars to give more importance to dynamic obstacles. In contrast to the usual LiDAR scan representations like range images and regular grid, this work uses a modern representation of LiDAR scans using permutohedral lattices. This representation gives ease of representing unstructured LiDAR points in an efficient lattice structure. We propose a machine learning approach to perform motion segmentation. The network architecture takes in two sequential point clouds and performs convolutions on them to estimate if 3D points from the first point cloud are moving or static. Using two temporal point clouds help the network in learning what features constitute motion. We have trained and tested our learning algorithm on the FlyingThings3D dataset and a modified KITTI dataset with simulated motion.

Given 2D images, it still remains a big challenge in the field of computer vision to group the image points into logical objects (segmentation) and to determine the locations in the scene (range estimation). Despite the decades of research, a single solution is yet to be found. Through our research we have demonstrated that a possible solution is to use moving aperture lens. This lens has the effect of introducing small, repeating movements of the camera center so that objects appear to translate in the image, by an amount that depends on distance from the plane of focus. Our novel method employs optical flow techniques to an image sequence, captured using a video camera with a moving aperture lens. For a stationary scene, optical flow magnitudes and direction are directly related to the three-dimensional object distance and location from the observer. Exploiting this information, we have successfully extracted objects at different depths and estimated the locations of objects in the scene, with respect to the plane of focus. Our work therefore demonstrates an ability for passive range estimation, without emitting any energy in an environment. Other potential applications include video compression, 3D video broadcast, teleconferencing and autonomous vehicle navigation. / Master of Science

This experiment investigated an individual's memory of specific motion events, unique actor, intrinsic motion, and extrinsic motion combination. Intrinsic motions involve the movement of an individual's body parts in a specific manner to move around, while extrinsic motions specify a path in reference to an external object. Participants viewed video clips, each depicting an actor performing a unique extrinsic and intrinsic motion combination. One week later, they viewed a different series of retrieval video clips consisting of old (identical to encoding), extrinsic conjunction (extrinsic motion previously performed by different actor), intrinsic conjunction (intrinsic motion previously performed by different actor), and new (novel extrinsic or intrinsic motion) video clips. Participants responded "yes" to viewing the old video clips the most often, followed by conjunction video clips, and then new video clips. Furthermore, there were a greater number of "yes" event memory recognition responses for extrinsic conjunction items than intrinsic conjunction items. / by Johanna D. Berger. / Thesis (M.A.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Satellite-framed languages and verb-framed languages differ in how they encode motion events. English encodes or lexicalizes Path in verb particles, prepositional phrases, or satellites associated with the main verb. In contrast, Turkish tends to encode Path in the main verb of a clause. When describing motion events, English speakers typically use verbs that convey information about manner rather than path, whereas Turkish speakers do the opposite. In this study, we investigated whether this crosslinguistic difference between English and Turkish influences how the speakers of these languages perform in a non-linguistic recognition memory task. In a video description task, English speakers used more manner verbs in the main verb of sentences than Turkish speakers did. In the recognition memory task, English speakers attended more strongly than Turkish speakers did to path of motion. English and Turkish speakers attended equally to manner of motion, however, providing no support for the linguistic relativity hypothesis. / by Ferhat Karaman. / Thesis (M.A.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.