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Moving obstacles have potentially higher risks of collision than stationary obstacles in traffic. Therefore, it is meaningful to detect moving obstacles by sensors equipped on a car for drive assistance applications. We propose two algorithms to detect moving obstacles using camera(s) depending on the relative motion between cameras and obstacles. Since camera(s) moves along with the subjective car,...
This paper proposes a real-time probabilistic solution to the problem of camera motion estimation in a video sequence. Instead of using explicit tracking of features, it only uses instantaneous image intensity variations without prior estimation of optical flow. We represent the camera motion as a probability density which is constructed from the individual motion densities, estimated from spatio-temporal...
Given a set of algorithms, which one(s) should you apply to, i) compute optical flow, or ii) perform feature matching? Would looking at the sequence in question help you decide? It is unclear if even a person with intimate knowledge of all the different algorithms and access to the sequence itself could predict which one to apply. Our hypothesis is that the most suitable algorithm can be chosen for...
We present a novel method for the discovery and statistical representation of motion patterns in a scene observed by a static camera. Related methods involving learning of patterns of activity rely on trajectories obtained from object detection and tracking systems, which are unreliable in complex scenes of crowded motion. We propose a mixture model representation of salient patterns of optical flow,...
This paper proposes a probabilistic graphical model for the problem of propagating labels in video sequences, also termed the label propagation problem. Given a limited amount of hand labelled pixels, typically the start and end frames of a chunk of video, an EM based algorithm propagates labels through the rest of the frames of the video sequence. As a result, the user obtains pixelwise labelled...
We discuss the cause of a severe optical flow estimation problem that fine motion structures cannot always be correctly reconstructed in the commonly employed multi-scale variational framework. Our major finding is that significant and abrupt displacement transition wrecks small-scale motion structures in the coarse-to-fine refinement. A novel optical flow estimation method is proposed in this paper...
State-of-the-art motion estimation algorithms suffer from three major problems: Poorly textured regions, occlusions and small scale image structures. Based on the Gestalt principles of grouping we propose to incorporate a low level image segmentation process in order to tackle these problems. Our new motion estimation algorithm is based on non-local total variation regularization which allows us to...
We present an adaptive weighted temporal averaging filter with implicit motion-compensation for effective object enhancement in sector scan sonar image sequences. Visual blurring artifacts introduced by the temporal filtering process due to motion of the sonar platform are minimized by accurate motion estimation and compensation. An algorithm is proposed to perform object boundary extraction for better...
Safe navigation through corridors plays a major role in the autonomous use of Micro Aerial Vehicles (MAVs) in indoor environments. In this paper, we present an approach for wall collision avoidance using a depth map based on optical flow from on board camera images. An omnidirectional fisheye camera is used as a primary sensor, while IMU data is needed for compensating rotational effects of the optical...
This paper proposes a simple window-based range flow method which uses isometry of the observed surface as its primary matching constraint. The method uses feature points as anchoring references of the surface deformation. Given a set of matched features no other intensity information is used and hence the method can tolerate intensity changes over time. The range-flow equation is only required for...
Optical flow is a motion field estimation method that has a wide range of applications. In this paper, we present a fully pipelined hardware architecture for high-speed optical flow estimation based on a full-search block matching algorithm. A census transform is applied to the corresponding pixels in the current and previous frame. The similarity between two census vectors within the search area...
The measurement of plant root growth is important in gauging the effect of experimental changes (nutrients, illumination, orientation, etc) made to plants. The most commonly used software is a combination of the common optical flow measurement method of Horn and Shunk with a ruggedized backward forward block matching method. The best results for this method show a low reliability for the optic flow...
Optical flow and its extensions have been widely used in motion detection and computer vision. In this paper, we apply principle component analysis (PCA) to analyze optical flows for better motion detection performance. The joint optical flow and PCA approach can efficiently detect moving objects and suppress small turbulence. It is effective in both static and dynamic background. It is particularly...
A novel video smoke recognition method based on optical flow is presented. The result of optical flow is assumed to be an approximation of motion field. The method is proposed as following, first, moving pixels and regions in the video are determined by a background estimation method. Then, a pyramidal implementation of the Lucas Kanade feature tracker is proposed to calculate the optical flow of...
The computation of optical flow in video sequences is a challenging task in most camera based scene interpretation systems. In the past, most optical flow computation algorithms has been either implemented in software running on general purpose processors or designed as an application specific hardware. However, these implementations either cannot support real-time processing requirements or result...
Optical flow can be expressed as the distribution of velocity vectors and it can be used as a method of describing the moving objects. The knowledge of the optical flow is valuable information in many applications including motion detection, object segmentation, and so on. In this paper, we use spatiotemporal differentiation method to estimate the optical flow. This method enable us to calculate the...
This paper proposes the identification of regions of interest in biospeckle patterns using unsupervised neural networks of the type Self-Organizing Maps. Segmented images are obtained from the acquisition and processing of laser speckle sequences. The dynamic speckle is a phenomenon that occurs when a beam of coherent light illuminates a sample in which there is some type of activity, not visible,...
A practical solution for counting traffic is analysed in this paper. The goal was to develop a solution that focuses on more CPU processing than on placing many complex sensors. This approach should reduce costs of both deployment and maintenance of such system. The proposed solution is to use video footage from a properly placed camera as input data. Vehicle movement is detected using optical flow...
In this paper we propose an optical flow estimation method based on compensating method by using spatiotemporal differentiation. Spatiotemporal differentiation enables us to calculate velocity distribution rapidly, but the error in the approximation of derivative coefficients increases as the displacement of the moving pattern between the successive frames becomes large. To expand the range of measurable...
This paper presents a vision based human machine interface (HMI) for the Xbox. It applies feature tracking algorithms to recognize user's head gestures and translates them into commands for the game. The pyramidal implementation of Lucas Kanade feature tracking is used to trace the optical flows in a sequence of frames. The experimental results show the feasibility of the proposed vision based interface,...
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