A Low Cost Moving Object Detection Method Using Boundary Tracking
25 May 2011-pp 127-136
TL;DR: This paper proposes a new method for moving object detection from video sequences by performing frame-boundary tracking and active-window processing leading to improved performance with respect to computation time and amount of memory requirements.
Abstract: Moving object detection techniques have been studied extensively for such purposes as video content analysis as well as for remote surveillance. Video surveillance systems rely on the ability to detect moving objects in the video stream which is a relevant information extraction step in a wide range of computer vision applications. There are many ways to track the moving object. Most of them use the frame differences to analyze the moving object and obtain object boundary. This may be quite resource hungry in the sense that such approaches require a large space and a lot of time for processing. This paper proposes a new method for moving object detection from video sequences by performing frame-boundary tracking and active-window processing leading to improved performance with respect to computation time and amount of memory requirements. A stationary camera with static background is assumed.
Citations
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01 Dec 2012
TL;DR: This paper proposes a new method for detection of orientation of a small vehicle from eight different orientations with respect to a stationery camera based on extracting features of a car object in terms of boundary description known as the signature.
Abstract: This paper proposes a new method for detection of orientation of a small vehicle from eight different orientations (e.g. front, front-left, front-right, side-left, side-right, back, back-left and back-right) of the vehicle with respect to a stationery camera. The proposed method is based on extracting features of a car object in terms of boundary description known as the signature. Matching of the signature with that of the templates stored in a database is performed towards deriving a dissimilarity value. This is used as a metric for classification. The proposed methodology may also be used for identifying the orientations of objects other than the cars.
4 citations
Cites background from "A Low Cost Moving Object Detection ..."
...The wide range of real life applications of object detection includes detection of moving objects from a video [1], detection of pedestrian and human being in a surveillance video, detection of car and other vehicles in a highway or parking area....
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25 Mar 2013
TL;DR: A method for detection of orientation of a human being from four different orientations with respect to a static camera and classification has been performed using the dissimilarity value as a metric.
Abstract: Detection of orientation of a human being with respect to a static camera is an important problem in computer vision. This paper proposes a method for detection of orientation of a human being from four different orientations (e.g. front, side-left, side-right, back) with respect to a static camera. In the proposed method, extraction of features of a human being has been performed in terms of boundary description known as the signature. A template database of four different human orientations has been created. The extracted features of a testing sample have been compared with that stored in the template database and a dissimilarity value has been calculated. The classification has been performed using the dissimilarity value as a metric. The results obtained are encouraging.
2 citations
Cites background from "A Low Cost Moving Object Detection ..."
...The scope of this research is huge with a wide range of real life applications which includes detection of moving objects from a video [2], detection of pedestrian and human being in a surveillance video [7], detection of car and other vehicles in a highway or parking area etc....
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01 Jan 2013
TL;DR: A novel technique for extracting human silhouette from video in real time using frame differencing technique followed by a number of steps for extraction of the human silhouette is proposed.
Abstract: Object classification from video is a well known topic of research in the context of computer vision. Video processing for the purpose of real time object classification and action recognition has great importance in building an intelligent surveillance system. This paper proposes a novel technique for extracting human silhouette from video in real time. Identification of the moving human objects is performed first using frame differencing technique followed by a number of steps for extraction of the human silhouette. The proposed method has been tested on a good number of videos having varying textured background with noise related to illumination change. The proposed method can also be applied for extraction of silhouettes of other types of animate and inanimate moving objects from a video with the view of object classification and recognition. The experimental results as documented in the paper establish the effectiveness of the proposed method.
2 citations
Journal Article•
TL;DR: The object tracking from a video sequence that contains moving objects is a critical task in real world applications and the proposed moving object tracking algorithm in which the moving object region can be extracted completely is considered.
Abstract: The object tracking from a video sequence that contains moving objects is a critical task in real world applications. Object tracking from video sequence is the process of locating moving objects in time using a camera. The purpose of object tracking is to determine the position of the object in images continuously and reliably against dynamic scenes. This paper concerned with the tracking and following of moving object in a sequence of frames from a video sequence. After preprocessing of the original video sequence the moving object is tracked with the proposed moving object tracking algorithm in which the moving object region can be extracted completely. Segmentation is performed to detect the object after reducing the noise from that scene. The bases of the work is the block matching object tracking algorithm for a moving target in a video by plotting a rectangular bounding box around it in each frame, and then process the data within that box to separate the tracked object from the background. The paper also includes experimental results of the tracking using the bounding box algorithm with certain improvements to make it suitable for tracking fast moving object.
References
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TL;DR: A general-purpose method is proposed that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames to improve object segmentation and background update.
Abstract: Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. The article proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.
1,521 citations
"A Low Cost Moving Object Detection ..." refers background in this paper
...It should react quickly to changes in background such as starting and stopping of vehicles [5, 6 ]....
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TL;DR: This paper surveys many existing schemes in the literature of background removal, surveying the common pre-processing algorithms used in different situations, presenting different background models, and the most commonly used ways to update such models and how they can be initialized.
Abstract: Identifying moving objects is a critical task for many computer vision applications; it provides a classification of the pixels into either foreground or background. A common approach used to achieve such classification is background removal. Even though there exist numerous of background removal algorithms in the literature, most of them follow a simple flow diagram, passing through four major steps, which are pre-processing, background modelling, foreground de- tection and data validation. In this paper, we survey many existing schemes in the literature of background removal, sur- veying the common pre-processing algorithms used in different situations, presenting different background models, and the most commonly used ways to update such models and how they can be initialized. We also survey how to measure the performance of any moving object detection algorithm, whether the ground truth data is available or not, presenting per- formance metrics commonly used in both cases.
424 citations
"A Low Cost Moving Object Detection ..." refers methods in this paper
...It offers an improvisation and enhancement of the basic background subtraction method [1, 4 ]. The authors assume a stationary video camera....
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TL;DR: Novel methods to evaluate the performance of object detection algorithms in video sequences are proposed and segmentation algorithms recently proposed are evaluated in order to assess how well they can detect moving regions in an outdoor scene in fixed-camera situations.
Abstract: In this paper, we propose novel methods to evaluate the performance of object detection algorithms in video sequences. This procedure allows us to highlight characteristics (e.g., region splitting or merging) which are specific of the method being used. The proposed framework compares the output of the algorithm with the ground truth and measures the differences according to objective metrics. In this way it is possible to perform a fair comparison among different methods, evaluating their strengths and weaknesses and allowing the user to perform a reliable choice of the best method for a specific application. We apply this methodology to segmentation algorithms recently proposed and describe their performance. These methods were evaluated in order to assess how well they can detect moving regions in an outdoor scene in fixed-camera situations
266 citations
"A Low Cost Moving Object Detection ..." refers methods in this paper
...It offers an improvisation and enhancement of the basic background subtraction method [ 1 , 4]. The authors assume a stationary video camera....
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...They rely on different assumptions e.g., statistical models of the background, minimization of Gaussian differences, minimum and maximum values, adaptability or a combination of frame differences and statistical background models [ 1 ]....
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TL;DR: In this work, a new approach to describe textural information in terms of redundant systems of functions is suggested, designed to be unaffected by scene type, background type or light conditions.
Abstract: This paper presents a new approach for shadow detection of moving objects in visual surveillance environment, improving localization, segmentation, tracking and classification of detected objects. An automatic segmentation procedure based on adaptive background difference is performed in order to detect potential shadow points so that, for all moving pixels, the approach evaluates the compatibility of photometric properties with shadow characteristics. The shadow detection approach is improved by evaluating the similarity between little textured patches, since shadow regions present same textural characteristics in each frame and in the corresponding adaptive background model. In this work we suggest a new approach to describe textural information in terms of redundant systems of functions. The algorithm is designed to be unaffected by scene type, background type or light conditions. Experimental results validate the algorithm's performance on a benchmark suite of indoor and outdoor video sequences.
204 citations
"A Low Cost Moving Object Detection ..." refers background in this paper
...It should react quickly to changes in background such as starting and stopping of vehicles [ 5 , 6]....
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15 Oct 2005
TL;DR: A fast and robust approach to the detection and tracking of moving objects based on using lines computed by a gradient-based optical flow and an edge detector and each tracked object has a state for handling occlusion and interference.
Abstract: We propose a fast and robust approach to the detection and tracking of moving objects. Our method is based on using lines computed by a gradient-based optical flow and an edge detector. While it is known among researchers that gradient-based optical flow and edges are well matched for accurate computation of velocity, not much attention is paid to creating systems for detecting and tracking objects using this feature. In our method, extracted edges by using optical flow and the edge detector are restored as lines, and background lines of the previous frame are subtracted. Contours of objects are obtained by using snakes to clustered lines. Detected objects are tracked, and each tracked object has a state for handling occlusion and interference. The experimental results on outdoor-scenes show fast and robust performance of our method. The computation time of our method is 0.089 s/frame on a 900 MHz processor.
161 citations
"A Low Cost Moving Object Detection ..." refers methods in this paper
...Local neighborhood similarity based approach [2] and contour based technique [ 3 ] for moving object detection are also found in the literature....
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