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Human's safety in construction areas is vital. A hard hat is required to enter a construction area. Stopping a person who is not wearing a hard hat entering a construction area is very important. Video-based surveillance to detect hard hat is a new solution to this safety problem. This paper brings different video processing techniques together to construct a framework for fast and robust hard hat...
Moving target classification plays a very important role in intelligent video surveillance system. A method for moving target classification in video sequences based on features combination and SVM is presented in this paper. In this method, single Gaussian background model based on the background difference method is used to achieve the motion detection, Hu moment features in moving target are extracted,...
Detection and classification of vehicles are the most challenging tasks of a video-based intelligent transportation system. Traditional detection and classification methods are based on subtraction of estimated still backgrounds from a video to find out the moving objects. In general, these methods are computationally highly expensive, and in many cases show poor detection and classification performance,...
In this paper, SVM is used in an electronic image stablizing algorithm designed for the sea environment with the horizon feature. First, samples updated mechanism proposed makes SVM an online classifier to distinguish two regions. On this basis, through the binary image processing and the Hough transform, the horizon is detected and the motion compensation values are acquired. Finally, the Affine...
In this paper we propose a coarse-to-fine method to detect pedestrians in video sequences. The detection process is divided into two stages: ROI (region of interest) generation stage and ROI classification stage. In the generation stage haar-like features are exploited to rapidly search the whole image and find interesting regions which may contain pedestrians. In the classification stage shapelet...
This paper presents a novel slice-based approach to detect pedestrians in still images. A pedestrian is divided into limited numbers of slice-based sub-regions through a spatio-temporal slice processing. First, sub-regions of interest are detected in different spatio-temporal slice images. Then, a clustering algorithm is proposed to combine these sub-regions into individual pedestrians based on their...
This paper proposes a novel human action recognition approach which represents each video sequence by a cumulative skeletonized images (called CSI) in one action cycle. Normalized-polar histogram corresponding to each CSI is computed. That is the number of pixels in CSI which is located in the certain distance and angles of the normalized circle. Using hierarchical classification in two levels, human...
In this paper, we explore the idea of using only pose, without utilizing any temporal information, for human action recognition. In contrast to the other studies using complex action representations, we propose a simple method, which relies on extracting “key poses” from action sequences. Our contribution is two-fold. Firstly, representing the pose in a frame as a collection of line-pairs, we propose...
A foreground extraction algorithm based on background subtraction and edge detection was proposed to obtain the foreground with a little change. An action classification method based on Enhanced Gait Energy Image (EGEI) and Locality Preserving Projections (LPP) was used. The high dimensional feature space was non-linearly reduced to lower dimensional space, which outperformed PCA and 2DPCA. The nearest-neighbor...
Automatic inspection on line plays an important role in industrial quality management nowadays. This paper proposes a new computer vision system for automatic steel surface inspection. The system analyzes the images sequentially acquired from steel bar to detect different kinds of defects on the steel surface. Several image processing strategies are used to detect and outline the defects. The detected...
A supervised softbot utilized for analyzing, segmenting, and properly classifying video clips pertaining to a wide variety of sporting events is presented. First, selected action scenes (i.e., training sequences) of a given sporting event are automatically segmented into real-world objects representing the participants of the activity. These objects correspond to the players, the playing field (or...
We propose a fully-automated mitosis event detector using hidden conditional random fields for cell populations imaged with time-lapse phase contrast microscopy. The method consists of two stages that jointly optimize recall and precision. First, we apply model-based microscopy image preconditioning and volumetric segmentation to identify candidate spatiotemporal sub-regions in the input image sequence...
This paper focuses on the issue of improving the quality of low level 2D feature extraction for human action recognition. For instance, existing algorithms such as the Optical Flow algorithm detects noisy and irrelevant features because of its lack of ground truth data sets for complex scenes. For these features, it is difficult to extract data such as coordinate positions of the features, velocity...
This paper describes the comparison of accuracy and performance of two machine learning approaches for visual object detection and tracking vehicles, from an on-road image sequence. The first is a neural network based approach. where an algorithm of multi resolution technique based on Haar basis functions was used to obtain an image with different scales. Thereafter a classification was carried out...
Currently, most research in vehicle color extraction is on a complete or static vehicle image or under a strict restriction on the position of vehicles and makes it hardly be applied in real-time applications. Firstly, new concepts such as First Sight Window and Second Sight Regions are proposed in this paper. Secondly, several algorithms are developed based on the first sight window and bottom-up...
We propose a region-based method to extract semantic foreground regions from color video sequences with static backgrounds. First, we introduce a new distance measure for background subtraction which is robust against shadows. Then the foreground region is extracted with a graph-based region segmentation method considering background difference and spatial homogeneity. For efficient computation, the...
Rapidly development in the realm of biometrics in recent years results in the unprecedented growth with the number of researchers and interesters. As one of the biometrics, gait recognition has many advantages such as from a distance, lower quality video, hard to disguised comparing with others. However, nearly all studies on gait recognition are 2D methods based on analysis of image sequences captured...
This paper presents a vision based scheme for detecting flying vehicle using a new feature extraction and correspondence algorithm as well as a motion flow vectors classifier. The base of detection is to classify the motion flow vectors of object and scene at two video sequences from a mobile monocular CCD camera. For this purpose, we introduce a method to extract robust features from fuzzified edges...
In this paper, a novel fusion method for gender classification from gait based on multi-view video sequences is proposed. At the feature level, each human silhouette in a whole gait period is segmented into eight different components. Then at the match score level, the discrimination distance of each corresponding component under every camera-view angle is respectively weighted. The two-dimension...
Detecting fiducial points successfully in facial images or video sequences can play an important role in numerous facial image interpretation tasks such as face detection and identification, facial expression recognition, emotion recognition, and face image database management. In this paper we propose an automatic and robust method of facial fiducial point's detection for facial expressions analysis...
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