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We address an essential problem in computer vision, that of unsupervised foreground object segmentation in video, where a main object of interest in a video sequence should be automatically separated from its background. An efficient solution to this task would enable large-scale video interpretation at a high semantic level in the absence of the costly manual labeling. We propose an efficient unsupervised...
Object Recognition is used to identify the object in the form of a video sequence or an image inside a video sequence or an image. The main objective of the Object Recognition is to correctly find the objects in the image or video sequence. There are different techniques are available to match the area. The goal is to identify the similar object or exact object in the input image. Template Matching...
This paper presents a novel algorithm for detection certain types of emergencies relating to fire, smoke and explosions by processing the data recorded from the camera monitoring, based on cascaded approach. First, the combination of Adaboost and Local binary pattern (LBP) are using for getting Region of Interest (ROI) and reducing time complexity. Next, to alleviate common problems of vulnerable...
The human detection and tracking in a video plays major roll in security systems. This paper proposes an approach to detect and track the persons in a video. This approach uses Gaussian Mixture Model to detect the person and Kalman filter to track the detected person. The processing time to detect the person is reduced by performing the detection operation on down-sampled video. After detecting the...
Moving object tracking is a tricky job in computer vision problems. In this approach, the object tracking system relies on the deterministic search of target, whose color content matches a reference histogram model. A simple RGB histogram-based color model is used to develop our observation system. Secondly and finally, we describe a new approach for moving object tracking with particle filter by...
Because of false detection rate, many fire detection algorithm cannot easily become a part of alarm monitoring system, so in this paper, we proposed to detect fire precisely with color variation in temporal domain to reduce false detection rate. By capturing special characteristic of fire in temporal domain, we can get reliable experimental result tested by 24 video sequences. With our algorithm,...
Multiple pedestrian tracking is an active and challenging research topic that many different approaches have addressed it. Since Human stick changes over time and person usually moving in random way, the identity association remains a hard task. In this paper we propose a new method for coupling detections over all the frames of the video sequence in order to make a performant tracker. The pedestrian...
This paper proposes a dark channel priori based approach for early stage smoke detection in video sequence. Firstly, smoke exhibit less chrominance components and chromaticity analysis is employed to detect grayish pixels in video sequences. Secondly, the motion history image(MHI) method is proposed to capture the motion characteristics of smoke. This moving history representation can be used to determine...
In this paper, an approach for video fire detection is proposed. The basic idea is fire has a highly varying texture and waits at the same location for long consecutive periods. Since pixel-wise framework has high algorithmic cost, a patch-wise periodical analysis of fire colored moving pixels in terms of consecutive re-occurrence in a given duration is presented. Instead of analyzing the fire motion...
The paper presents technical aspects related to the video computer analysis of people movement in the sales rooms. Automatic generation of the people density maps (also referred to as the “heat maps”) is helpful for the analysis of customers' and employees' behavior in sale and office rooms advertising effectiveness research or tracking movements. Generation of the density maps was realized with the...
This paper presents a new method to track and count vehicles in video traffic sequences. The proposed method uses image processing, particle filtering, and motion coherence to group particles in videos, forming convex shapes that are analyzed for potential vehicles. This analysis takes into consideration the convex shape of the objects and background information to merge or split the groupings. After...
In this paper, we proposed a super pixel based depth map propagation algorithm for the application in 2D to 3D video conversion. The proposed algorithm employs four main processes to generate depth maps for all frames in video sequences. First, the depth map of the key frames in the input sequences are generated by manual work. Second, the frames in the input sequences are over-segmented by Simple...
This paper proposes an original method for video indexing based on a spatio-temporal segmentation scheme. The basic idea is to extract salient regions from the video content and use them as scene descriptors for indexing. The obtained results confirm the efficiency of the proposed approach and open new perspectives for video summarizing and indexing.
Video Object Segmentation (VOS) is to cut out a selected object from video sequences, where the main difficulties are shape deformation, appearance variations and background clutter. To cope with these difficulties, we propose a novel method, named as Hierarchical Localized Classification of Regions (HLCR). We suggest that appearance models as well as the spatial and temporal coherence between frames...
This paper illustrates a simple, yet effective semi-automated object segmentation framework over video sequences. This is through an extension of the GrowCut framework, an image segmentation scheme based on cellular automata. We describe how GrowCut is extended to video sequences, as well as providing our own improvements and addressing problematic areas to the original formulation. This provides...
The detection and tracking of faces and facial features in video sequences is a fundamental and challenging problem in computer vision. This research area has many applications in face identification systems, model-based coding, gaze detection, human-computer interaction, teleconferencing, etc. This work presents a real time system for detection and tracking of facial features in video sequences....
Experts-Shift is a novel statistical framework for keyframe-based video segmentation. Compared to existing video segmentation techniques with simple color models, our method proposes a probability mixture model coupling strong image classifiers (experts) with latent spatial configuration. In order to propagate image labels to the successive frames, our algorithm track all experts jointly by a efficient...
This paper presented a video moving object segmentation and tracking system based on the active contour and the color classification models. First, the active contour model is applied to segment the target object in the initial frame. From the segmented object, the object and background regions are extracted. Then the object and the background regions are separately clustered according to color feature...
Background subtraction of video sequences is mainly regarded as a solved problem. However, no complete benchmark about Background Subtraction Algorithms (BSA) has been established, with ground truth and associated quality measures. One of the reasons is that such comparative study needs annotated datasets. In this article, we propose a BSA evaluation dataset built from realistic synthetic image and...
Color tone detection accomplishes the modelization of a color cluster for a set of pixels that present a hue similar to a particular one, which is being detected. Image pixels can be classified according to their membership to the particular color class through such cluster modelization. Such approaches can be employed in different computer vision application fields. Nevertheless few proposals in...
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