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Maneuvering target tracking is a big challenge to the performance of a visual tracker. The paper proposes a method to keep the tracker robust to target maneuvering by selecting discriminative features from a large feature space, and constructing a velocity motion model with adaptive noise variance. Furthermore, the feature selection procedure is embedded into the particle filtering process with the...
Background subtraction is a process of separating moving foreground objects from the non-moving background. This technique must adapt to the illumination, motion and the geometry background changes such as shadow, reflections, and etc. In this paper, one of the traditional background subtraction techniques which is frame differencing (FD) is conducted to detect the moving object in outdoor environment...
This paper presents a framework to track non-rigid objects adaptively by fusion of visual and motional feature descriptors. The proposed technique can automatically detect an object from different points of view as soon as the object starts moving. Moreover an object model is created and gradually updated using both new and previous features. As a result, the proposed technique is able to track a...
Moving cast shadow removal is an important yet difficult problem in video analysis and applications. This paper presents a novel algorithm for detection of moving cast shadows, that based on a local texture descriptor called Scale Invariant Local Ternary Pattern (SILTP). An assumption is made that the texture properties of cast shadows bears similar patterns to those of the background beneath them...
This paper considers the problem of detecting actions from cluttered videos. Compared with the classical action recognition problem, this paper aims to estimate not only the scene category of a given video sequence, but also the spatial-temporal locations of the action instances. In recent years, many feature extraction schemes have been designed to describe various aspects of actions. However, due...
Human detection and recognition at a distance is recently a matter of great concern among computer vision researchers. This paper introduces a new set of human body features for the recognition of detected human as an object. The feature extraction is performed by an established human model consisting of five parts. These features consist of geometric calculations of detected object and their different...
We proposed a method for automatic detection and tracking of moving object employing a particle filter in conjunction with a color feature method. The particle filtering is used because it is robust for non-linear and non-Gaussian dynamic state estimation problems and performs well when clutter and occlusions are present on the image. A histogram-based framework is used to describe the color feature...
Smoke detection in video surveillance images has been studied for years. However, given an image in open or large spaces with typical smoke and the disturbance of commonly moving objects such as pedestrians or vehicles, robust and efficient smoke detection is still a challenging problem. In this paper, we present a novel and reliable framework for automatic smoke detection. It exploits three features:...
We conduct subjective tests to evaluate the performance of scalable video coding with different spatial-domain bit-allocation methods, visual attention models, and motion feature extractors in the literature. For spatial-domain bit allocation, we use the selective enhancement and quality layer assignment methods. For characterizing visual attention, we use the motion attention model and perceptual...
This paper presents a Directional Rectangular Pattern (DRP) based complex background modeling method to detect the moving objects in a video sequence. Different from Local Binary Pattern (LBP) encoding the binary result of first-order derivative between the central point and its neighborhoods, Directional Rectangular Pattern is proposed to encode the binary result of first and second order derivative...
Background modeling is one of the most important parts of visual surveillance systems. Most background models are pixel-based which extract detailed shape of moving objects, but they are so sensitive to non-stationary scenes. In many applications there is no need to detect the detailed shape of moving objects. So some researchers use block-based methods instead of pixel-based which are more insensitive...
Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motion-based tracking framework which makes no such assumptions. Object is represented by a set of local invariant features, whose motions are observed by a feature correspondence process. A generative model is proposed to depict the relationship...
Both improper initialization and fake Gaussian components are critical problems in GMM-based foreground detection. The former can lead to a poor local maximum, while the latter invokes unhandled disturbance. To eliminate these destructive impacts, two kinds of feedback knowledge are introduced: positive and negative prior. For appropriate initialization, high level modules provide the positive prior...
Indoor and outdoor real-time monitoring of scenes has become a hot topic for decades. This paper analyzes the median filter and its shortcomings and proposes a modified detection method for moving objects with complex background. Using the method based on decision-making data gives the weights of a consecutive sequence of frames to calculate the weighted average of the corresponding values of pixel...
Motion segmentation is a very critical task in video surveillance system. In this paper, we propose a novel approach to detect moving objects in a complex background. Gaussian mixture model (GMM) is an effective way to extract moving objects from a video sequence. However, the conventional mixture Gaussian method suffers from false motion detection in complex backgrounds and slow convergence. This...
In this paper, we propose spatio-temporal silhouette representations, called silhouette energy image (SEI) and silhouette history image (SHI) to characterize motion and shape properties for recognition of human movements such as human actions, activities in daily life. The SEI and SHI are constructed by using the silhouette image sequence of an action. The span or difference of the end time and start...
We propose an evolving scheme to detect slow as well as fast moving objects in a video sequence. The proposed scheme employ both spatio-temporal and temporal segmentation to obtain the video object plane and hence detection. We propose a compound Markov random field model as the a priori image model that takes into account the spatial distribution of the current frame, temporal frames and the edge...
This paper proposes a novel tracking strategy that can robustly track an object within a fixed environment. We define a robust model-based tracker using Kalman filtering combined with recursive least squares. The tracking is done by fitting successively more elaborate models on the tracked region and the segmentation is done by extracting the regions of the image that are consistent with the computed...
Detection of moving objects is the first step in many applications using video sequences like video-surveillance, optical motion capture and multimedia application. The process mainly used is the background subtraction which one key step is the foreground detection. The goal is to classify pixels of the current image as foreground or background. Some critical situations as shadows, illumination variations...
A new algorithm is proposed for background subtraction in highly dynamic scenes. Background subtraction is equated to the dual problem of saliency detection: background points are those considered not salient by suitable comparison of object and background appearance and dynamics. Drawing inspiration from biological vision, saliency is defined locally, using center-surround computations that measure...
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