Algorithms for cooperative multisensor surveillance

Collins, Robert T.; Lipton, A.J.; Fujiyoshi, Hironobu; Kanade, Takeo

doi:10.1109/5.959341

Cited by 540 publications

(324 citation statements)

References 71 publications

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“…In recent years, problems related to distributed surveillance have received an increasing amount of attention from researchers in academia, government laboratories and industry (Liu et al, 2000;Collins et al, 2001;Foresti and Snidaro, 2003;Tseng et al, 2005;Settembre et al, 2008;Miao, 2010;Elmogy, 2010). This research activity has borne some fruit in tackling some of the challenging problems, which are still open.…”

Section: Introductionmentioning

confidence: 99%

Cooperation in distributed surveillance systems for dense regions

Benaskeur

Khamis

Irandoust

2011

IJIDSS

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Abstract:In this paper, we envision a distributed surveillance system where spatially distributed sensors of different modalities can sense collaboratively and continuously large-scale, dynamic, dense and semi-structured environments. The paper discusses surveillance operations in complex and dense environments, such as littoral regions, and describes the key features of distributed surveillance systems. Effective and robust cooperation among the artificial and/or the human agents can synergistically improve the performance of these systems and can endow them with higher-level faculties, such as cooperative target detection and tracking. Different forms of cooperative activities in distributed surveillance systems are mentioned. The paper focuses on intra and inter-platform target cueing and handoff as augmentative forms of cooperation in distributed surveillance.

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Section: Introductionmentioning

confidence: 99%

Cooperation in distributed surveillance systems for dense regions

Benaskeur

Khamis

Irandoust

2011

IJIDSS

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“…[7,8,9], for multi-camera tracking with overlapping field of views. These methods usually require camera calibration and environmental models to track targets.…”

Section: Related Workmentioning

confidence: 99%

Inter-camera Association of Multi-target Tracks by On-Line Learned Appearance Affinity Models

Kuo

Huang

Nevatia

2010

Lecture Notes in Computer Science

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Abstract. We propose a novel system for associating multi-target tracks across multiple non-overlapping cameras by an on-line learned discriminative appearance affinity model. Collecting reliable training samples is a major challenge in on-line learning since supervised correspondence is not available at runtime. To alleviate the inevitable ambiguities in these samples, Multiple Instance Learning (MIL) is applied to learn an appearance affinity model which effectively combines three complementary image descriptors and their corresponding similarity measurements. Based on the spatial-temporal information and the proposed appearance affinity model, we present an improved inter-camera track association framework to solve the "target handover" problem across cameras. Our evaluations indicate that our method have higher discrimination between different targets than previous methods.

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“…The server module co-ordinates device activity, archives/retrieves data and provides the interface with a human operator. The design of a surveillance system with no server to avoid this centralization is reported in [16]. All the independent subsystems are completely self-contained, and all these nodes are then set up to communicate with each other without having a mutually shared communication point.…”

Section: Introductionmentioning

confidence: 99%

Data fusion to improve trajectory tracking in a Cooperative Surveillance Multi-Agent Architecture

2010

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In this paper we present an extension of Cooperative Surveillance Multi-Agent System (CS-MAS) architecture to incorporate dynamic coalition formation. A specific coalition formation using fusion skills is shown so that the fusion process is distributed now in two layers: (i) a global layer in the fusion center, which initialize the coalitions and (ii), local layer within coalitions, with the dynamic instantiation of a local fusion agent. There are several types of autonomous agents: surveillancesensor agents, fusion center agent, local fusion agent, interface agents, record agents, planning agents, etc. Autonomous agents differ in their ability to carry out a specific surveillance task. A surveillance-sensor agent controls and manages individual sensors (usually video cameras). It has different capabilities depending on its functional complexity and limitation related to specific sensor nature aspects. In this work we add a new autonomous agent called local fusion agent to the CS-MAS architecture, addressing specific problems of on-line sensor alignment, registration, bias removal and data fusion. The local fusion agent it is dynamically created by the fusion center agent and involves several surveillance-sensor agents working in a coalition. We show how the inclusion of this new dynamic local fusion agent guarantee that, in a video-surveillance system, objects of interest are successfully tracked across the whole area, assuring continuity and seamless transitions.

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Algorithms for cooperative multisensor surveillance

Abstract: The Video Surveillance and Monitoring (VSAM)

Cited by 540 publications

References 71 publications

Cooperation in distributed surveillance systems for dense regions

Cooperation in distributed surveillance systems for dense regions

Inter-camera Association of Multi-target Tracks by On-Line Learned Appearance Affinity Models

Data fusion to improve trajectory tracking in a Cooperative Surveillance Multi-Agent Architecture

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