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An algorithm using Bayesian on-line learning for object based video image segmentation is proposed in this paper. First the strengths of image pixel's spatial location, color and motion segments are fused in one framework for image clustering and segmentation. Here the appropriate modeling of Probability Distribution Functions(PDF) of each feature cluster is obtained through Gaussian Distribution...
Robust and accurate background segmentation is crucial for surveillance applications and is a key element in visual tracking, layer-based compression, and silhouette-based 3D reconstruction. In this paper, we present a novel spatial-temporal model that describes the appearance and dynamics of background scenes at multiple resolutions. We propose a time-dependent Markov Random Field (MRF) to represent...
Surveillance applications often capture video over long time periods; interpretation of this data is facilitated by background models that effectively represent the typical behavior in the scene. Capturing statistics of the spatio-temporal derivatives at each pixel can efficiently model surprisingly complicated motion patterns. Considering the video as a function of space and time, the mean 3D structure...
Instead of the conventional background and foreground definition, we propose a novel method that decomposes a scene into time-varying background and foreground intrinsic images. The multiplication of these images reconstructs the scene. First, we form a set of previous images into a temporal scale and compute their spatial gradients. By taking advantage of the sparseness of the filter outputs, we...
Moving object detection is very important for video surveillance. In many environments, motion maybe either interesting (salient) motion (e.g., a person) or uninteresting motion (e.g., swaying branches.) In this paper, we propose a new real-time algorithm to detect salient motion in complex environments by combining temporal difference imaging and a temporal filtered motion field. We assume that the...
Automatic detection of moving objects is a fundamental problem in computer vision. Motion analysis, object recognition, and video surveillance applications often depend on reliable segmentation of moving objects against a fixed background. Although shadows move in the scene with the objects that cast them, it is often important that only objects in motion, and not their shadows, are detected. For...
This paper proposes a method of clustering video frame pixels for a moving object extraction system. Two cascaded classifiers work cooperatively to firstly classify the pixels into background and non-background cluster and then classify the non-background cluster into four clusters. Besides the moving cluster and shadow cluster, two additional clusters, corresponding to the noisy highlighting pixels...
In this paper we propose an approach to count the number of pedestrians, given a trajectory data set provided by a tracking system. The tracking process itself is treated as a black box providing us the input data. The idea is to apply a hierarchical clustering algorithm, using different data representations and distance measures, as a post-processing step. The final goal is to reduce the difference...
Many video surveillance and identification applications need to find moving objects in the field of view of a stationary camera. A popular method for obtaining these silhouettes is through the process of background subtraction. We present a novel method for comparing image frames to the model of the stationary background that exploits the spatial and temporal dependencies that objects in motion impose...
This paper presents a unified approach to adaptive target detection and tracking. The unifying concept is "coherent motion energy", a measure of the extent to which a single motion dominates local spatiotemporal structure. There are three major components to the approach. First, a multiresolution analysis of coherent motion energy is used to detect salient dynamic targets. Second, a robust...
Region-based tracking in a temporal image sequence is described as a segmentation of current frame into a set of non-overlapping regions: the tracking regions and the non-tracking region. The segmentation is viewed to be a Markov labeling process. Based on the key idea of using a doubly stochastic prior model, the optimal estimation for the label field is found by the minimization of a differentiable...
We present a monocular object tracker, able to detect and track multiple objects in non-controlled environments. Bayesian per-pixel classification is used to build a tracking framework that segments an image into foreground and background objects, based on observations of object appearances and motions. Gaussian mixtures are used to build the color appearance models. The system adapts to changing...
Traffic monitoring systems based on image and sequence analyses are widely employed in Intelligent Transportation Systems (ITS's) in order to analyze traffic parameters and statistics. To this purpose, tracking objects is often needed. However, occlusions can mislead a vehicle tracking system based on a single camera, thus resulting in tracking errors. In this work we present a vehicle tracking algorithm...
In this paper we present a new method for tracking rigid objects using a modified version of the Active Appearance Model. Unlike most of the other appearance-based methods in the literature, our method allows for both partial and self occlusion of the objects. We use ground-truth to demonstrate the accuracy of our tracking algorithm. We show that our method can be applied to track moving objects over...
As tracking systems become more effective at reliably tracking multiple objects over extended periods of time within single camera views and across overlapping camera views, increasing attention is being focused on tracking objects through periods where they are not observed. This paper investigates an unsupervised hypothesis testing method for learning the characteristics of objects passing unobserved...
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