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Human motion recognition is traditionally approached by either recognizing basic motions from features derived from video input or by interpreting complex motions by applying a high-level hierarchy of motion primitives. The former method is usually limited to rather simple motions while the latter requires human expert knowledge to build up a suitable hierarchy. In this paper we propose a new approach...
In this paper, an object detection and tracking algorithm is proposed. At the object detection stage, the spatial features of object are extracted by wavelet transform, according to frame difference, the moving target is determined. In order to effectively utilize the temporal motion information, Markov random field prior probability model and the observation field model are established, taking advantage...
In this paper, we present a novel descriptor to characterize human action when it is being observed from a far field of view. Visual cues are usually sparse and vague under this scenario. An action sequence is divided into overlapped spatial-temporal volumes to make reliable and comprehensive use of the observed features. Within each volume, we represent successive poses by time series of Histogram...
The video sequences degraded by fog suffer from poor visibility. In this paper, we present a contrast limited adaptive histogram equalization (CLAHE)-based method to remove fog. CLAHE establishes a maximum value to clip the histogram and redistributes the clipped pixels equally to each gray level. It can limit the noise while enhancing the contrast. First, the background image is extracted from the...
Different facial expressions are related to a small set of muscles and limited ranges of motions. In this paper we propose an automatic facial expression recognition system, different from other automatic methods in both face detection and feature extraction. In system the facial expressions identify itself in video sequences. First, the differences between neutral and emotional states are detected...
A fast duplicate video detection system based on camera transitional behavior and the suffix array data structure is proposed in this work. The main idea is to match video clips according to their temporal structures, and frames corresponding to unique events are marked as anchor frames. To simplify the detection process, we use the camera transitional behavior to indicate unique events. Specifically,...
We propose a nonlinear covariance region descriptor for target tracking. The target object appearance and spatial information is represented using a covariance matrix in a target derived Hilbert space using kernel principal component analysis. A similarity measure is derived, which computes the similarity of a candidate image region to the learned covariance matrix. A variational technique is provided...
An improved lossless data hiding scheme, which can restore the original image completely after data extraction, is presented in this paper. In the proposed method, a cover image is reordered into a sequence using space filling curves. For each pixel (except the first pixel) in the sequence, a pixel difference is generated between the pixel and its neighbor pixel. Due to the inherent structure of images...
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...
This paper proposes an effective lane detection and tracking method using statistical modeling of lane color and edge-orientation in the image sequence. At first, we will address some problem of classifying a pixel into two classes(lane or background) and detecting one exact lane. Generally, the probability of a pixel classification error conditioned on the distinctive feature vector can be decreased...
In this paper SVM algorithm is applied to classify the scenery video types in compressed domain. Firstly we extract video sequences randomly from scenery video and detect representative frames from the video sequences; secondly we extract features such as color layout, dominant color, edge histogram and face feature; then according to SVM, representative frames are classified as natural scenery, personality,...
It is important to protect children from harmful effects of objectionable materials, such as pornography, which are now prevalent on the Internet. In this paper, a new method from the feature porno-sounds recognition point of view is proposed to detect adult video sequences automatically which serves as a complementary approach to the recognition method from image's point of view. To the special of...
A multi-object tracking algorithm is proposed for road & bridge traffic scene. Firstly, background reconstruction was conducted based on a statistical model, and the background was updated using Kalman filter at regular intervals. Secondly, the background differencing was conducted to obtain potential objects. An improved mean-shift tracking algorithm was put forwarded for image sequences without...
This paper provides a comprehensive quantitative comparison of metrics for detecting visual anomalies between two videos that are recorded along same path but at different times by a camera on a patrolling platform. The metrics used in this paper are histogram based metrics, statistic based metrics and pixel differences based metrics. We test the metrics for the detection of mobile and stationary...
In this paper we propose a new method for human action categorization by using an effective combination of novel gradient and optic flow descriptors, and creating a more effective codebook modeling the ambiguity of feature assignment in the traditional bag-of-words model. Recent approaches have represented video sequences using a bag of spatio-temporal visual words, following the successful results...
This paper presents a new approach for event detection from video surveillance data based on optical fow histogram with no prior knowledge of the motion nature. First,we start by estimating the motion from images sequence using optical flow technique. Second, we perform a classification using the histogram of the optical flow vectors and we use a chain coding algorithm that we applied to each class...
A real-time video-based fire smoke detection method that can be incorporated with a automatic monitoring system for early alerts is proposed by this paper. The successive processing steps of our real-time algorithm are using the motion history segmentation algorithm to register the possible fire smoke position in a video and then analyze the spectral, spatial and temporal characteristics of the fire...
An approach is proposed for abnormal sections detection in video sequences. In this approach, firstly the histogram is selected to describe the color change in the section, and then the histograms of the frames selected from the section compose the histogram matrix. In order to improve the process efficiency, the principal components analysis (PCA) is used to reduce dimensions of the histogram matrix...
In this paper, we propose a gait analysis method which extracts the dynamic and static information from human walking for walking path and identity recognition. First, we utilize the periodicity of swing distances to estimate the gait period for each gait sequence. For each gait cycle, we extract the dynamic information by analyzing the statistic histogram of motion vectors and static information...
Approaches to single image categorization do not easily generalize to natural time-varying image sequences. In natural environments, object categories tend to have few features that help to distinguish between each other and the surrounding environment. To better discriminate between categories and the surrounding environment, we propose a multi-view categorization approach that exploits the statistics...
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