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Book ChapterDOI

Real-Time Distributed Multi-object Tracking in a PTZ Camera Network

TL;DR: This paper proposes a novel framework for real-time, distributed, multi-object tracking in a PTZ camera network with this capability and provides a tool to mark an object of interest such that the object is tracked at a certain size as it moves in the view of various cameras across space and time.
Abstract: A visual surveillance system should have the ability to view an object of interest at a certain size so that important information related to that object can be collected and analyzed as the object moves in the area observed by multiple cameras. In this paper, we propose a novel framework for real-time, distributed, multi-object tracking in a PTZ camera network with this capability. In our framework, the user is provided a tool to mark an object of interest such that the object is tracked at a certain size as it moves in the view of various cameras across space and time. The pan, tilt and zoom capabilities of the PTZ cameras are leveraged upon to ensure that the object of interest remains within the predefined size range as it is seamlessly tracked in the PTZ camera network. In our distributed system, each camera tracks the objects in its view using particle filter tracking and multi-layered belief propagation is used for seamlessly tracking objects across cameras.
Citations
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Journal ArticleDOI
TL;DR: The uncertainty bound for the multiple Gaussian functions, termed multiple Gaussians Uncertainty (MGU), is proved, which significantly generalizes the uncertainty principle for the single Gaussian function.
Abstract: This paper proves the uncertainty bound for the multiple Gaussian functions, termed multiple Gaussians Uncertainty (MGU), which significantly generalizes the uncertainty principle for the single Gaussian function. First, as a theoretical contribution, we prove that the momentum (velocity) and position for the sum of multiple Gaussians wave function are theoretically bounded. Second, as for a practical application, we show that the bound can be well exploited for object tracking to detect anomalies of local movement in an online learning framework. By integrating MGU with a given object tracker, we demonstrate that uncertainty principle can provide remarkable robustness in tracking. Extensive experiments are done to show that the proposed MGU can significantly help base trackers overcome the object drifting and reach state-of-the-art results.

76 citations

Journal ArticleDOI
TL;DR: In this study, the authors discuss various issues and problems in video analytics, proposed solutions and present some of the important current applications of video analytics.
Abstract: Video, rich in visual real-time content, is however, difficult to interpret and analyse. Video collections necessarily have large data volume. Video analytics strives to automatically discover patterns and correlations present in the large volume of video data, which can help the end-user to take informed and intelligent decisions as well as predict the future based on the patterns discovered across space and time. In this study, the authors discuss various issues and problems in video analytics, proposed solutions and present some of the important current applications of video analytics.

12 citations

Journal ArticleDOI
TL;DR: A multiple-object tracking approach in largescale scene based on visual sensor network based on an improved particle filter method to improve the tracking precision and the cumulative error generated from evaluating particles is eliminated through an appearance model.
Abstract: In this paper, a multiple-object tracking approach in largescale scene is proposed based on visual sensor network. Firstly, the object detection is carried out by extracting the HOG features. Then, object tracking is performed based on an improved particle filter method. On the one hand, a kind of temporal and spatial dynamic model is designed to improve the tracking precision. On the other hand, the cumulative error generated from evaluating particles is eliminated through an appearance model. In addition, losses of the tracking will be incurred for several reasons, such as occlusion, scene switching and leaving. When the object is in the scene under monitoring by visual sensor network again, object tracking will continue through object re-identification. Finally, continuous multiple-object tracking in large-scale scene is implemented. A database is established by collecting data through the visual sensor network. Then the performances of object tracking and object re-identification are tested. The effectiveness of the proposed multiple-object tracking approach is verified. key words: visual sensor network, HOG, improved particle filter, reidentification, object tracking

1 citations


Cites methods from "Real-Time Distributed Multi-object ..."

  • ...used particle filter tracking to track the objects in each camera view and multi-layered belief propagation for seamlessly tracking objects across cameras [2]....

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Book ChapterDOI
19 Nov 2022
TL;DR: In this article , an event-triggered mechanism that controls how much information is sent was proposed to account for the scene density and the data association module was integrated into an existing distributed multi-person multi-camera tracking system.
Abstract: Distributed tracking systems have several benefits over centralized setups such as faster processing time and greater robustness to failures. However, the practical deployment of a distributed multi-camera multi-target tracking system poses other important challenges. In this work, we address two of these practical problems. The first one is the spatial and temporal identification of the targets in the network, i.e., the data association problem. To solve it, we propose to build intelligent and adaptive local appearance models of each target that only store the most relevant information. The second problem is the intensive use of bandwidth caused by the periodic communications that each camera requires for the cooperative tracking and the data association of all the targets. In the paper, we manage the bandwidth usage with an event-triggered mechanism that controls how much information is sent. The main novelty of our mechanism is to account for the scene density, coupling it with the data association module and enhancing it. We integrate the new modules into an existing distributed multi-person multi-camera tracking system and demonstrate their benefits on different public benchmarks of increasing difficulty.
References
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Proceedings ArticleDOI
23 Jun 1999
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Abstract: A common method for real-time segmentation of moving regions in image sequences involves "background subtraction", or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian, distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.

7,660 citations


"Real-Time Distributed Multi-object ..." refers background or methods in this paper

  • ...Since the cameras that view the entry/exit areas are static for a certain time period, these cameras apply background subtraction [12] to detect objects that enter the area under observation....

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  • ...As an object enters the area under observation, it is detected using background subtraction [12] and represented by its bounding box....

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Book ChapterDOI
28 May 2002
TL;DR: This work introduces a new Monte Carlo tracking technique based on the same principle of color histogram distance, but within a probabilistic framework, and introduces the following ingredients: multi-part color modeling to capture a rough spatial layout ignored by global histograms, incorporation of a background color model when relevant, and extension to multiple objects.
Abstract: Color-based trackers recently proposed in [3,4,5] have been proved robust and versatile for a modest computational cost They are especially appealing for tracking tasks where the spatial structure of the tracked objects exhibits such a dramatic variability that trackers based on a space-dependent appearance reference would break down very fast Trackers in [3,4,5] rely on the deterministic search of a window whose color content matches a reference histogram color modelRelying on the same principle of color histogram distance, but within a probabilistic framework, we introduce a new Monte Carlo tracking technique The use of a particle filter allows us to better handle color clutter in the background, as well as complete occlusion of the tracked entities over a few framesThis probabilistic approach is very flexible and can be extended in a number of useful ways In particular, we introduce the following ingredients: multi-part color modeling to capture a rough spatial layout ignored by global histograms, incorporation of a background color model when relevant, and extension to multiple objects

1,549 citations


"Real-Time Distributed Multi-object ..." refers methods in this paper

  • ...Similar to [5], the distance between the reference color model and the color model of the current frame is calculated using the Bhattacharya distance given by Eq....

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  • ...Similar to [5], we consider the Hue-Saturation-Value (HSV) color histogram of the bounding box to represent the measurement model that is robust with respect to illumination changes....

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Journal ArticleDOI
01 Oct 2001
TL;DR: This paper presents an overview of the issues and algorithms involved in creating this semiautonomous, multicamera surveillance system and its potential to improve the situational awareness of security providers and decision makers.
Abstract: The Video Surveillance and Monitoring (VSAM) team at Carnegie Mellon University (CMU) has developed an end-to-end, multicamera surveillance system that allows a single human operator to monitor activities in a cluttered environment using a distributed network of active video sensors. Video understanding algorithms have been developed to automatically detect people and vehicles, seamlessly track them using a network of cooperating active sensors, determine their three-dimensional locations with respect to a geospatial site model, and present this information to a human operator who controls the system through a graphical user interface. The goal is to automatically collect and disseminate real-time information to improve the situational awareness of security providers and decision makers. The feasibility of real-time video surveillance has been demonstrated within a multicamera testbed system developed on the campus of CMU. This paper presents an overview of the issues and algorithms involved in creating this semiautonomous, multicamera surveillance system.

693 citations


"Real-Time Distributed Multi-object ..." refers background in this paper

  • ...In recent times, research on multi-camera tracking in camera networks consisting of static cameras as well as PTZ camera networks has been gaining importance [1,3]....

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Proceedings Article
13 Oct 2003
TL;DR: This paper proposes to model the target distribution as a nonparametric mixture model, and presents the general tracking recursion in this case, and shows how a Monte Carlo implementation of the general recursion leads to a mixture of particle filters that interact only in the computation of the mixture weights, leading to an efficient numerical algorithm.
Abstract: In recent years particle filters have become a tremendouslypopular tool to perform tracking for non-linearand/or non-Gaussian models. This is due to their simplicity,generality and success over a wide range of challengingapplications. Particle filters, and Monte Carlo methodsin general, are however poor at consistently maintainingthe multi-modality of the target distributions that may arisedue to ambiguity or the presence of multiple objects. Toaddress this shortcoming this paper proposes to model thetarget distribution as a non-parametric mixture model, andpresents the general tracking recursion in this case. It isshown how a Monte Carlo implementation of the generalrecursion leads to a mixture of particle filters that interactonly in the computation of the mixture weights, thus leadingto an efficient numerical algorithm, where all the resultspertaining to standard particle filters apply. The ability ofthe new method to maintain posterior multi-modality is illustratedon a synthetic example and a real world trackingproblem involving the tracking of football players in a videosequence.

448 citations


"Real-Time Distributed Multi-object ..." refers methods in this paper

  • ...Based on the detected object, we initialize the particle filter tracker [16] in these cameras....

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  • ...Then, the mixture filtering distribution is of the form given in [16],...

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Proceedings ArticleDOI
01 Jan 2003
TL;DR: In this paper, the target distribution is modeled as a nonparametric mixture model, and the general tracking recursion is used to maintain the posterior multimodality of the target distributions.
Abstract: In recent years particle filters have become a tremendously popular tool to perform tracking for nonlinear and/or nonGaussian models This is due to their simplicity, generality and success over a wide range of challenging applications Particle filters, and Monte Carlo methods in general, are however poor at consistently maintaining the multimodality of the target distributions that may arise due to ambiguity or the presence of multiple objects To address this shortcoming this paper proposes to model the target distribution as a nonparametric mixture model, and presents the general tracking recursion in this case It is shown how a Monte Carlo implementation of the general recursion leads to a mixture of particle filters that interact only in the computation of the mixture weights, thus leading to an efficient numerical algorithm, where all the results pertaining to standard particle filters apply The ability of the new method to maintain posterior multimodality is illustrated on a synthetic example and a real world tracking problem involving the tracking of football players in a video sequence

374 citations