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Identifying the boundary of the optic cup excavation is of critical importance in the assessment of glaucomatous risk. Currently, most approaches are focused on the use of pallor. We present an automatic method to determine the cup excavation boundary based on vessel kinking in non-stereo retinal fundus images. The method tracks vessels using a self-initialized evolving model which adapts during propagation...
Estimation of human motion has been improved by recent advances in depth sensors such as the Microsoft Kinect. However, they often have limited range of depths and a large number of such sensors are necessary to estimate motion in large areas. In this paper, we explore the possibility of estimating motion from monocular data using initial and intermittent 3D models provided by the depth sensor. We...
We propose a novel method for automatic detection of the transport mode of a person carrying a Smart-phone. Existing approaches assume idealized positioning data with no GPS signal losses, require information from additional external sources such as real time bus locations, or only allow for a coarse distinction between very few categories (e.g. ‘still’, ‘walk’, ‘motorized’). Our approach is designed...
Under-water video-mosaics are an important tool e.g. for inspection of man-made infrastructure. Cameras may drift in rotation and distance to the surface while the mosaic will often be very much larger than a single frame resulting in long chains of planar homographies. This contribution addresses the problems arising from dead-reckoning drift in such chains. Local patches are rectified using homography...
This paper proposes a method to estimate earthquake ground motion by analyzing the video taken with a fixed surveillance camera. In recent years, a network of seismometers was constructed to observe the ground motion. However, the spatial density of the seismometers was not high enough to obtain an adequate spatial distribution of the earthquake ground motion, which varies depending on the local ground...
In this paper, we address the problem of reconstructing 3D trajectories given only 2D point projection trajectories of an articulated structure. Hitherto, most applications of articulated trajectory reconstruction require: (i) relative lengths, and (ii) camera motion. We propose a novel extension that allows us to in many circumstances circumvent these limitations. Of particular note is our characterisation...
This paper introduces an algorithm for dense motion segmentation of pedestrians in crowded video sequences. This algorithm realizes dense and temporally-consistent segmentation under severe occlusion conditions. To segment the whole appearance of each articulated object, the algorithm uses temporal invariance of geodesic distance (similarity) between segments as a criterion for motion segmentation...
Many accidents at intersection are happend due to judgment errors, which are caused from blind spots of drivers. We propose a method for generating warning messages related to the accidents predicted between blind spots of drivers and vehicle behaviors by using surveillance camera and on-board camera. In this method, vehicle behaviors of straight, right turn, left turn, change to right lane, and change...
In this paper, we propose a novel unsupervised online learning trajectory analysis method based on weighted directed graph. Each trajectory can be represented as a sequence of key points. In the training stage, unsupervised expectation-maximization algorithm (EM) is applied for training data to cluster key points. Each class is a Gaussian distribution. It is considered as a node of the graph. According...
This paper introduces a novel video presentation term spatial-temporal pyramid sparse coding (STPSC) which characterizes both the spatial and temporal aspects of the video. Specifically, the co-occurrences of visual words are computed with respect to the spatial layout and the sequencing of the features in the video. The representation captures both the spatial arrangement and the temporal relationship...
In this paper, a new refined sparse subspace clustering (RSSC) method is proposed for robust motion segmentation. Given a set of trajectories of tracked feature points from multiple moving object, RSSC aims at seeking a sparse representation (SR) for each trajectory with respect to a recovered low-rank dictionary. The segmentation of motion is obtained by applying spectral clustering to the affinity...
By segmenting moving objects out and then densely stitching them into background frames, video synopsis provides an efficient way to condense long videos while preserving most activities. Existing video synopsis methods, however, often suffer from either high computation cost due to global energy minimization or unsatisfactory condense rate to avoid loss of important object activities. To address...
Complementary information, when combined in the right way, is capable of improving clustering and segmentation problems. In this paper, we show how it is possible to enhance motion segmentation accuracy with a very simple and inexpensive combination of complementary information, which comes from the column and row spaces of the same measurement matrix. We test our approach on the Hopkins155 dataset...
Progress in LiDAR scanning has led to the availability of large scale LiDAR datasets for urban areas. We use such pre-acquired data to determine the poses of 2D monocular cameras highly accurately in real-time. This is achieved by first correctly aligning key-frames of the multi-modal data using a combination of feature and intensity-based 2D/3D registration methods. The online pose is then determined...
This paper presents a map matching method based on an ideal Hidden Markov Model (HMM) to find a sequence of roads that corresponds to a given sequence of raw GPS points. Our method is a simplification of the more-complex HMM-based method that maintains its capabilities to cope with the noises and sparsity of the raw GPS data. We test the method with the real-world raw GPS data that is publicly available...
We propose a novel trajectory clustering algorithm which is suitable for online processing of pedestrian or vehicle trajectories computed with a vision-based tracker. Our approach does not require defining distances between trajectories, and can thus process broken trajectories which are inevitable in most cases when object trackers are applied to real world video footage. Clusters are defined as...
Understanding fish behavior by extracting normal motion patterns and then identifying abnormal behaviors is important for understanding the effects of environmental change. In the literature, there are many studies on normal/abnormal behavior detection in the areas of human behaviour analysis, traffic surveillance, and nursing home surveillance, etc. However, the literature is very limited in terms...
Figure-ground labeling is a classical problem in computer vision in which the goal is to label different parts of the visual input as figural or background. Yet most existing approaches focuses on single image figure-ground labeling with little emphasis on video. We present a method which integrates several cues to achieve figure-ground labeling on video sequences. The method is evaluated on challenging...
This paper presents a novel method for pedestrian counting in surveillance videos, which localizes and tracks the head-shoulders of pedestrians via the integrated bottom-up/top-down processes. In the bottom-up stage, we extract and match informative local image features crossing frames to obtain the initial moving regions (i.e. potential pedestrians). The top-down stage comprises two steps: (i) head-shoulder...
Detection of moving vehicles in wide area motion imagery (WAMI) is increasingly important, with promising applications in surveillance, traffic scene understanding and public service applications such as emergency evacuation and policy security. However, the large camera motion, along with low contrast between vehicles and backgrounds, makes detection a challenging task. In this paper, we propose...
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