Kalman filtering based object tracking in surveillance video system
01 Jan 2011-
TL;DR: In this paper, a moving object tracking in surveillance video using Kalman filter is proposed and the ability of tracking occluded moving object will be added to increase the efficiency during tracking.
Abstract: In the field of motion estimation for surveillance video, various techniques have been applied. One of the common approaches is Kalman filtering technique and it is interesting to explore the extension of this technique for the prediction and estimation of motion via the image sequences. In this paper, a moving object tracking in surveillance video using Kalman filter is proposed. The typical Kalman filter is good in tracking the position of a moving object. However, when dealing with occlusion, the typical Kalman filter is not able to keep tracking and predicting the position of the occluded moving object. During occlusion, the information of moving object is not available for detection and tracking. The lacking of occlusion scene determination and prediction ability cause the existing Kalman filter fails in tracking occluded object. Besides that, in the case of tracking multiple moving objects, existing Kalman filter will experience difficulties to identify the respective objects. Therefore, in order to encounter these problems, an object tracking method using enhanced Kalman filter will be developed. The ability of tracking occluded moving object will be added to increase the efficiency during tracking. Furthermore, object recognition feature will be added too to increase the accuracy of the object tracking system.
Citations
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01 Jan 2016
TL;DR: Gaussian Mixture Model (GMM) method was applied for vehicle detection and Kalman Filter method was applications for object tracking and the result shows that the detection system gets 100% for the precision value, sensitivity and accuracy.
Abstract: Intelligent Transport System (ITS) is a method used in traffic arrangements to make efficient road transport system. One of the ITS application is the detection and tracking of vehicle objects. In this research, Gaussian Mixture Model (GMM) method was applied for vehicle detection and Kalman Filter method was applied for object tracking. The data used are vehicles video under two different conditions. First condition is light traffic and second condition is heavy traffic. Validation of detection system is conducted using Receiver Operating Characteristic (ROC) analysis. The result of this research shows that the light traffic condition gets 100% for the precision value, 94.44% for sensitivity, 100% for specificity, and 97.22% for accuracy. While the heavy traffic condition gets 75.79% for the precision value, 88.89% for sensitivity, 70.37% for specificity, and 79.63% for accuracy. With avarage consistency of Kalman Filter for object tracking is 100%.
28 citations
TL;DR: In this research subjective quality assessment of object detection and object tracking is discussed in detail and the proposed system eliminates the shadow and provides 79% accuracy.
Abstract: Digital image processing is one of the most researched fields nowadays. The ever increasing need of surveillance systems has further on made this field the point of emphasis. Surveillance systems are used for security reasons, intelligence gathering and many individual needs. Object tracking and detection is one of the main steps in these systems. Different techniques are used for this task and research is vastly done to make this system automated and to make it reliable. In this research subjective quality assessment of object detection and object tracking is discussed in detail. In the proposed system the background subtraction is done from the clean original image by using distortion of color and brightness. The subtracted image is then tracked using connected component labeling. The proposed system eliminates the shadow and provides 79% accuracy.
15 citations
Cites background or methods from "Kalman filtering based object track..."
...It is a widely-used recursive technique for tracking linear dynamical systems under Gaussian noise [3]....
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...The video analysis can be safely stated to be consisting of three steps: 1 detecting the objects that are interested in, 2 the frame to frame tracking of those objects and 3 the analysis of the path traversed to analyze their behavior [3]....
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Posted Content•
TL;DR: The modular Crowd Simulation Evaluation through Composition framework (CSEC) as mentioned in this paper provides a quantitative comparison between different pedestrian and crowd simulation approaches based on the comparison of source footage against synthetic video created through novel composition techniques.
Abstract: In this work we present the modular Crowd Simulation Evaluation through Composition framework (CSEC) which provides a quantitative comparison between different pedestrian and crowd simulation approaches. Evaluation is made based on the comparison of source footage against synthetic video created through novel composition techniques. The proposed framework seeks to reduce the complexity of simulation evaluation and provide a platform from which the comparison of differing simulation algorithms as well as parametric tuning can be conducted to improve simulation accuracy or providing measures of similarity between crowd simulation algorithms and source data. Through the use of features designed to mimic the Human Visual System (HVS), specific simulation properties can be evaluated relative to sample footage. Validation was performed on a number of popular crowd datasets and through comparisons of multiple pedestrian and crowd simulation algorithms.
4 citations
19 Mar 2015
TL;DR: An automatic human tracking spotlight using image processing is explained in this paper and has 2-degrees-of-freedom and provides a tilting motion which facilitates tracking.
Abstract: An automatic human tracking spotlight using image processing is explained in this paper. The spotlight is used to track a person continuously on a stage. It has 2-degrees-of-freedom and provides a tilting motion which facilitates tracking. The position of the person is detected using a camera with the help of image processing. Tracking is done using Kalman filter algorithm. The spotlight is mounted on the ceiling for illuminating the person on the stage.
3 citations
06 Dec 2017
TL;DR: Within this chapter the emerging topic of automated risk assessment in a domestic scene is discussed, state of the art techniques are reviewed followed by developed methodologies which focus on safer human and robotic interactions with an environment.
Abstract: Within this chapter the emerging topic of automated risk assessment in a domestic scene is discussed, state of the art techniques are reviewed followed by developed methodologies which focus on safer human and robotic interactions with an environment. By using the risk estimation framework, the notion of a quantitative risk score is presented. Hazards within a scene are evaluated and measured using risk elements which provide a numeric representation of specific types of risk. Emphasis is given to the concept of risk as a result of interaction with an environment, specifically whether human or robotic actions in a scene can effect overall risk. To this end, techniques which simulate human or robotic behaviour with regard to risk in an environment are reviewed. Specifically the ideas of interaction and visibility are addressed defining risk in terms of areas within a scene that are visited most often and which are least visible. As with any behaviour simulation techniques, validation of their accuracy is required and a number of simulation evaluation techniques are reviewed. Finally a conclusion as to the current state of automated risk assessment is given, with a brief look at the future of the research area.
2 citations
Cites background from "Kalman filtering based object track..."
...These features are based on the optical flow [36], Histogram of Oriented Optical Flow (HOOF) [37] and tracklets [38] of the moving objects in both sequences and are designed to emulate the way in which humans perceive motion....
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References
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TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
28,073 citations
"Kalman filtering based object track..." refers methods in this paper
...Canny Edge detector [2] is the most popular edge detection method....
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01 Jan 2011
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Abstract: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
14,708 citations
"Kalman filtering based object track..." refers methods in this paper
...A commonly used interest point detectors include Moravec’s interest operator [3], Harris interest point detector [4], KLT detector [5], and SIFT detector [6]....
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01 Jan 1988
TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
Abstract: The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.
13,993 citations
"Kalman filtering based object track..." refers methods in this paper
...A commonly used interest point detectors include Moravec’s interest operator [3], Harris interest point detector [4], KLT detector [5], and SIFT detector [6]....
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21 Jun 1994
TL;DR: A feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world are proposed.
Abstract: No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments. >
8,432 citations
"Kalman filtering based object track..." refers methods in this paper
...A commonly used interest point detectors include Moravec’s interest operator [3], Harris interest point detector [4], KLT detector [5], and SIFT detector [6]....
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TL;DR: This work studies the motion correspondence problem for which a diversity of qualitative and statistical solutions exist, and presents a tracking algorithm that satisfies these-possibly constrained-models in a greedy matching sense, including an effective way to handle detection errors and occlusion.
Abstract: Studies the motion correspondence problem for which a diversity of qualitative and statistical solutions exist. We concentrate on qualitative modeling, especially in situations where assignment conflicts arise either because multiple features compete for one detected point or because multiple detected points fit a single feature point. We leave out the possibility of point track initiation and termination because that principally conflicts with allowing for temporary point occlusion. We introduce individual, combined, and global motion models and fit existing qualitative solutions in this framework. Additionally, we present a tracking algorithm that satisfies these-possibly constrained-models in a greedy matching sense, including an effective way to handle detection errors and occlusion. The performance evaluation shows that the proposed algorithm outperforms existing greedy matching algorithms. Finally, we describe an extension to the tracker that enables automatic initialization of the point tracks. Several experiments show that the extended algorithm is efficient, hardly sensitive to its few parameters, and qualitatively better than other algorithms, including the presumed optimal statistical multiple hypothesis tracker.
410 citations
"Kalman filtering based object track..." refers methods in this paper
...The deterministic methods use qualitative motion heuristics [7] to constrain the correspondence problem while the statistical methods explicitly take the object measurement and take uncertainties into account to establish correspondence....
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