Results 1 - 10
of
121
Object Tracking: A Survey
, 2006
"... The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns o ..."
Abstract
-
Cited by 701 (7 self)
- Add to MetaCart
The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... In this paper, we address the issue of tracking moving objects in an environment covered by multiple uncalibrated cameras with overlapping fields of view, typical of most surveillance setups. In such a scenario, it is essential to establish correspondence between tracks of the same object, seen in d ..."
Abstract
-
Cited by 124 (3 self)
- Add to MetaCart
(Show Context)
In this paper, we address the issue of tracking moving objects in an environment covered by multiple uncalibrated cameras with overlapping fields of view, typical of most surveillance setups. In such a scenario, it is essential to establish correspondence between tracks of the same object, seen in di#erent cameras, to recover complete information about the object. We call this the problem of consistent labeling of objects when seen in multiple cameras. We employ a novel approach of finding the limits of field of view (FOV) of each camera as visible in the other cameras. We show that if the FOV lines are known, it is possible to disambiguate between multiple possibilities for correspondence. We present a method to automatically recover these lines by observing motion in the environment. Furthermore, once these lines are initialized, the homography between the views can also be recovered. We present results on indoor and outdoor sequences, containing persons and vehicles.
Appearance Modeling for Tracking in Multiple Non-overlapping Cameras
- In IEEE International Conference on Computer Vision and Pattern Recognition
, 2005
"... When viewed from a system of multiple cameras with nonoverlapping fields of view, the appearance of an object in one camera view is usually very different from its appearance in another camera view due to the differences in illumination, pose and camera parameters. In order to handle the change in o ..."
Abstract
-
Cited by 101 (2 self)
- Add to MetaCart
(Show Context)
When viewed from a system of multiple cameras with nonoverlapping fields of view, the appearance of an object in one camera view is usually very different from its appearance in another camera view due to the differences in illumination, pose and camera parameters. In order to handle the change in observed colors of an object as it moves from one camera to another, we show that all brightness transfer functions from a given camera to another camera lie in a low dimensional subspace and demonstrate that this subspace can be used to compute appearance similarity. In the proposed approach, the system learns the subspace of intercamera brightness transfer functions in a training phase during which object correspondences are assumed to be known. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both location and appearance cues. We evaluate the proposed method under several real world scenarios obtaining encouraging results. 1.
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... This paper presents a framework for finding point correspondences in monocular image sequences over multiple frames. The general problem of multi-frame point correspondence is NP Hard for three or more frames. A polynomial time algorithm for a restriction of this problem is presented and is used a ..."
Abstract
-
Cited by 85 (7 self)
- Add to MetaCart
(Show Context)
This paper presents a framework for finding point correspondences in monocular image sequences over multiple frames. The general problem of multi-frame point correspondence is NP Hard for three or more frames. A polynomial time algorithm for a restriction of this problem is presented and is used as the basis of the proposed greedy algorithm for the general problem. The greedy nature of the proposed algorithm allows it to be used in real time systems for tracking and surveillance etc. In addition, the proposed algorithm deals with the problems of occlusion, missed detections and false positives by using a single non-iterative greedy optimization scheme, and hence reduces the complexity of the overall algorithm as compared to most existing approaches where multiple heuristics are used for the same purpose. While most greedy algorithms for point tracking do not allow for entry and exit of the points from the scene, this is not a limitation for the proposed algorithm. Experiments with real and synthetic data over a wide range of scenarios and system parameters are presented to validate the claims about the performance of the proposed algorithm.
Inference of non-overlapping camera network topology by measuring statistical dependence
- IN PROC. IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION
, 2005
"... We present an approach for inferring the topology of a camera network by measuring statistical dependence between observations in different cameras. Two cameras are considered connected if objects seen departing in one camera are seen arriving in the other. This is captured by the degree of statisti ..."
Abstract
-
Cited by 60 (2 self)
- Add to MetaCart
(Show Context)
We present an approach for inferring the topology of a camera network by measuring statistical dependence between observations in different cameras. Two cameras are considered connected if objects seen departing in one camera are seen arriving in the other. This is captured by the degree of statistical dependence between the cameras. The nature of dependence is characterized by the distribution of observation transformations between cameras, such as departure to arrival transition times, and color appearance. We show how to measure statistical dependence when the correspondence between observations in different cameras is unknown. This is accomplished by non-parametric estimates of statistical dependence and Bayesian integration of the unknown correspondence. Our approach generalizes previous work which assumed restricted parametric transition distributions and only implicitly dealt with unknown correspondence. Results are shown on simulated and real data. We also describe a technique for learning the absolute locations of the cameras with Global Positioning System (GPS) side information.
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
"... Abstract—This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a ge ..."
Abstract
-
Cited by 57 (11 self)
- Add to MetaCart
(Show Context)
Abstract—This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a generic topographical scene description from underlying motion structure as observed over time. The scene topology is automatically learned and is distinguished by points of interest and motion characterized by activity paths. The methods we review are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction. Index Terms—Event detection, motion analysis, situational awareness, statistical learning. Fig. 1. Relationship between analysis levels and required knowledge: highlevel activity analysis requires large amounts of domain knowledge while lowlevel analysis assumes very little. I.
Learning a multi-camera topology
- in Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance
, 2003
"... “©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other w ..."
Abstract
-
Cited by 53 (4 self)
- Add to MetaCart
(Show Context)
“©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”
Correspondence-free activity analysis and scene modeling in multiple camera views
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2010
"... Abstract—We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. In this paper, we refer to activities as motion patterns of objects, which correspond to paths in far-field scenes. We assume that the topology of cameras is unknown and quite ar ..."
Abstract
-
Cited by 40 (4 self)
- Add to MetaCart
(Show Context)
Abstract—We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. In this paper, we refer to activities as motion patterns of objects, which correspond to paths in far-field scenes. We assume that the topology of cameras is unknown and quite arbitrary, the fields of views covered by these cameras may have no overlap or any amount of overlap, and objects may move on different ground planes. Using low-level cues, objects are first tracked in each camera view independently, and the positions and velocities of objects along trajectories are computed as features. Under a probabilistic model, our approach jointly learns the distribution of an activity in the feature spaces of different camera’s views. Then it accomplishes the following tasks: (1) grouping trajectories, which belong to the same activity but may be in different camera views, into one cluster; (2) modeling paths commonly taken by objects across multiple camera views; (3) detecting abnormal activities. Advantages of this approach are that it does not require first solving the challenging correspondence problem, and that learning is unsupervised. Even though correspondence is not a prerequisite, after the models of activities have been learnt, they can help to solve the correspondence problem, since if two trajectories in different camera views belong to the same activity, they are likely to correspond to the same object. Our approach is evaluated on a simulated data set and two very large real data sets, which have 22, 951 and 14, 985 trajectories respectively. Index Terms—Visual surveillance, Activity analysis in multiple camera views, Correspondence, Clustering. 1
A stochastic approach to tracking objects across multiple cameras
- Australian Conference on Artificial Intelligence
, 2004
"... Abstract. This paper is about tracking people in real-time as they move through the non-overlapping fields of view of multiple video cameras. The paper builds upon existing methods for tracking moving objects in a single camera. The key extension is the use of a stochastic transition matrix to descr ..."
Abstract
-
Cited by 31 (3 self)
- Add to MetaCart
(Show Context)
Abstract. This paper is about tracking people in real-time as they move through the non-overlapping fields of view of multiple video cameras. The paper builds upon existing methods for tracking moving objects in a single camera. The key extension is the use of a stochastic transition matrix to describe people’s observed patterns of motion both within and between fields of view. The parameters of the model for a particular environment are learnt simply by observing a person moving about in that environment. No knowledge of the environment or the configuration of the cameras is required. 1
Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding
, 2010
"... We propose a novel approach to understanding activities from their partial observations monitored through multiple non-overlapping cameras separated by unknown time gaps. In our approach, each camera view is first decomposed automatically into regions based on the correlation of object dynamics acr ..."
Abstract
-
Cited by 30 (16 self)
- Add to MetaCart
We propose a novel approach to understanding activities from their partial observations monitored through multiple non-overlapping cameras separated by unknown time gaps. In our approach, each camera view is first decomposed automatically into regions based on the correlation of object dynamics across different spatial locations in all camera views. A new Cross Canonical Correlation Analysis (xCCA) is then formulated to discover and quantify the time delayed correlations of regional activities observed within and across multiple camera views in a single common reference space. We show that learning the time delayed activity correlations offers important contextual information for (i) spatial and temporal topology inference of a camera network; (ii) robust person re-identification and (iii) global activity interpretation and video temporal segmentation. Crucially, in contrast to conventional methods, our approach does not rely on either intra-camera or inter-camera object tracking; it thus can be applied to low-quality surveillance videos featured with severe inter-object occlusions. The effectiveness and robustness of our approach are demonstrated through experiments on 330 hours of videos captured from 17 cameras installed at two busy underground stations with complex and diverse scenes.
