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124
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 ..."
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Cited by 701 (7 self)
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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.
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
"... Abstract—Occlusion and lack of visibility in crowded and cluttered scenes make it difficult to track individual people correctly and consistently, particularly in a single view. We present a multiview approach to solve this problem. In our approach, we neither detect nor track objects from any singl ..."
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Cited by 54 (0 self)
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Abstract—Occlusion and lack of visibility in crowded and cluttered scenes make it difficult to track individual people correctly and consistently, particularly in a single view. We present a multiview approach to solve this problem. In our approach, we neither detect nor track objects from any single camera or camera pair; rather, evidence is gathered from all of the cameras into a synergistic framework and detection and tracking results are propagated back to each view. Unlike other multiview approaches that require fully calibrated views, our approach is purely image-based and uses only 2D constructs. To this end, we develop a planar homographic occupancy constraint that fuses foreground likelihood information from multiple views to resolve occlusions and localize people on a reference scene plane. For greater robustness, this process is extended to multiple planes parallel to the reference plane in the framework of plane to plane homologies. Our fusion methodology also models scene clutter using the Schmieder and Weathersby clutter measure, which acts as a confidence prior, to assign higher fusion weight to views with lesser clutter. Detection and tracking are performed simultaneously by graph cuts segmentation of tracks in the space-time occupancy likelihood data. Experimental results with detailed qualitative and quantitative analysis are demonstrated in challenging multiview crowded scenes. Index Terms—Tracking, sensor fusion, graph-theoretic methods. Ç 1
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2006
"... Visual surveillance using multiple cameras has attracted increasing interest in recent years. Correspondence between multiple cameras is one of the most important and basic problems which visual surveillance using multiple cameras brings. In this paper, we propose a simple and robust method, based ..."
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Cited by 48 (0 self)
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Visual surveillance using multiple cameras has attracted increasing interest in recent years. Correspondence between multiple cameras is one of the most important and basic problems which visual surveillance using multiple cameras brings. In this paper, we propose a simple and robust method, based on principal axes of people, to match people across multiple cameras. The correspondence likelihood reflecting the similarity of principal axis pairs is constructed according to the relationship between “ground-points ” detected in each camera view and the intersections of principal axes detected in different camera views and transformed to the same view. Our method has the following desirable properties: (1) Camera calibration is not needed. (2) Accurate motion detection and segmentation are less critical due to the robustness of the principal axis-based feature to noise. (3) Based on the fused data derived from correspondence results, positions of people in each camera view can be accurately located even when the people are partially occluded in all views. The experimental results on several real video sequences from outdoor environments have demonstrated the effectiveness, efficiency and robustness of our method.
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 ..."
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Cited by 40 (4 self)
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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 general method for sensor planning in multi-sensor systems: Extension to random occlusion
, 2005
"... Abstract. Systems utilizing multiple sensors are required in many domains. In this paper, we specifically concern our-selves with applications where dynamic objects appear randomly and the system is employed to obtain some user-specified characteristics of such objects. For such systems, we deal wit ..."
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Cited by 33 (1 self)
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Abstract. Systems utilizing multiple sensors are required in many domains. In this paper, we specifically concern our-selves with applications where dynamic objects appear randomly and the system is employed to obtain some user-specified characteristics of such objects. For such systems, we deal with the tasks of determining measures for evaluating their performance and of determining good sensor configurations that would maximize such measures for better system performance. We introduce a constraint in sensor planning that has not been addressed earlier: visibility in the presence of random occluding objects. Two techniques are developed to analyze such visibility constraints: a probabilistic approach to determine “average ” visibility rates and a deterministic approach to address worst-case scenarios. Apart from this constraint, other important constraints to be considered include image resolution, field of view, capture orientation, and algorithmic constraints such as stereo matching and background appearance. Integration of such constraints is performed via the development of a probabilistic framework that allows one to reason about different occlusion events and integrates different multi-view capture and visibility constraints in a natural way. Integration of the thus obtained capture quality measure across the region of interest yields a measure for the effectiveness of a sensor configuration and maximization of such measure yields sensor configurations that are
Determining vision graphs for distributed camera networks using feature digests
- EURASIP Journal on Advances in Signal Processing 2007
, 2007
"... We propose a method for obtaining the vision graph for a distributed camera network, in which each camera is represented by a node, and an edge appears between two nodes if the two cameras jointly image a sufficiently large part of the environment. The technique is decentralized, requires no orderin ..."
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Cited by 32 (3 self)
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We propose a method for obtaining the vision graph for a distributed camera network, in which each camera is represented by a node, and an edge appears between two nodes if the two cameras jointly image a sufficiently large part of the environment. The technique is decentralized, requires no ordering on the set of cameras, and assumes that cameras can only communicate a finite amount of information with each other in order to establish the vision graph. Each camera first detects a large number of feature points that are approximately scale- and viewpoint-invariant. Both the number of features and the length of each feature descriptor are substantially reduced to form a fixed-length “feature digest” that is broadcast to the rest of the network. Each receiver camera decompresses the feature digest to recover approximate feature descriptors, robustly estimates the epipolar geometry to reject incorrect matches and grow additional ones, and decides whether sufficient evidence exists to form a vision graph edge. We use receiver-operating-characteristics (ROC) curves to analyze the performance of different message formation schemes, and show that high detection rates can be achieved while maintaining low false alarm rates. Finally, we show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We demonstrate the accurate performance of the vision graph generation and camera calibration algorithms using a simulated 60-node outdoor camera network. In this simulation, we achieved vision graph edge detection probabilities exceeding 0.8 while maintaining false alarm probabilities below 0.05. I.
Surveillance camera scheduling: A virtual vision approach,” VSSN ’05
- Proceedings of the third ACM international workshop on Video surveillance & sensor networks
, 2005
"... We present a surveillance system, comprising wide field-of-view (FOV) passive cameras and pan/tilt/zoom (PTZ) active cameras, which automatically captures and labels high-resolution videos of pedestrians as they move through a designated area. A wide-FOV stationary camera can track multiple pedestri ..."
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Cited by 28 (13 self)
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We present a surveillance system, comprising wide field-of-view (FOV) passive cameras and pan/tilt/zoom (PTZ) active cameras, which automatically captures and labels high-resolution videos of pedestrians as they move through a designated area. A wide-FOV stationary camera can track multiple pedestrians, while any PTZ active camera can capture high-quality videos of a single pedestrian at a time. We propose a multi-camera control strategy that com-bines information gathered by the wide-FOV cameras with weighted round-robin scheduling to guide the available PTZ cameras, such that each pedestrian is viewed by at least one active camera during their stay in the designated area. A distinctive centerpiece of our work is the exploitation of a vi-sually and behaviorally realistic virtual environment simulator for the development and testing of surveillance systems. Our research would be more or less infeasible in the real world given the im-pediments to deploying and experimenting with an appropriately complex camera sensor network in a large public space the size of, say, a train station. In particular, we demonstrate our surveil-lance system in a virtual train station environment populated by autonomous, lifelike virtual pedestrians, wherein easily reconfig-urable virtual cameras generate synthetic video feeds that emu-late those generated by real surveillance cameras monitoring richly populated public spaces.
Vezzani: A multi-camera vision system for fall detection and alarm generation
- Expert Systems, Volume 24 Issue 5
"... Abstract- In-house video surveillance can represent an excellent support for people with some difficulties (e.g., elderly or disabled people) living alone and with a limited autonomy. New hardware technologies and in particular digital cameras are now affordable and they have recently gained credit ..."
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Cited by 27 (1 self)
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Abstract- In-house video surveillance can represent an excellent support for people with some difficulties (e.g., elderly or disabled people) living alone and with a limited autonomy. New hardware technologies and in particular digital cameras are now affordable and they have recently gained credit as tools for (semi-)automatically assuring the people’s safety. In this paper a multi camera vision system for detecting and tracking people and recognizing dangerous behaviors and events such as a fall is presented. In such a situation a suitable alarm can be sent, for example by means of a SMS. A novel technique of warping people’s silhouette is proposed to exchange visual information between partially overlapped cameras whenever a camera handover occurs. Finally, a multi-client and multi-threaded transcoding video server delivers live video streams to operators/remote users in order to check the validity of a received alarm. Semantic and event based transcoding algorithms are used to optimize the bandwidth usage. A tworoom setup has been created in our lab to test the performance of the overall system and some of the obtained results are reported. I.
Eyes in the Sky: Decentralized Control for the Deployment of Robotic Camera Networks
, 2010
"... This paper presents a decentralized control strategy for positioning and orienting multiple robotic cameras to collectively monitor an environment. The cameras may have various degrees of mobility from six degrees of freedom, to one degree of freedom. The control strategy is proven to locally minim ..."
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Cited by 26 (3 self)
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This paper presents a decentralized control strategy for positioning and orienting multiple robotic cameras to collectively monitor an environment. The cameras may have various degrees of mobility from six degrees of freedom, to one degree of freedom. The control strategy is proven to locally minimize a novel metric representing information loss over the environment. It can accommodate groups of cameras with heterogeneous degrees of mobility (e.g. some that only translate and some that only rotate), and is adaptive to robotic cameras being added or deleted from the group, and to changing environmental conditions. The robotic cameras share information for their controllers over a wireless network using a specially designed networking algorithm. The control strategy is demonstrated in repeated experiments with three flying quadrotor robots indoors, and with five flying quadrotor robots outdoors. Simulation results for more complex scenarios are also presented.
