Activity Topology Estimation for Large Networks of Cameras

Hengel, Anton van den; Dick, Anthony; Hill, Rhys

doi:10.1109/avss.2006.17

Cited by 23 publications

(10 citation statements)

References 8 publications

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“…It is worth pointing out that van den Hengel et al (2006) also attempted to infer the camera topology without relying on object tracking. Their method starts with full connectivity among camera views and gradually eliminates linkages among image regions that exhibit simultaneous object occupancy and vacancy.…”

Section: Previous Workmentioning

confidence: 99%

“…To facilitate more efficient multi-camera surveillance and reduce the burden on human operators, growing research efforts have been undertaken on automated activity understanding in camera networks, focusing on camera topology inference (Makris et al 2004;Tieu et al 2005;van den Hengel et al 2006), person re-identification (Javed et al 2003(Javed et al , 2005Prosser et al 2008;Gheissari et al 2006;Gray and Tao 2008;Hu et al 2006a;Zheng et al 2009), and global activity analysis (Lee et al 2000;Wang et al 2010;Zelniker et al 2008). In topology inference, the aim is to infer spatial and temporal relationships between cameras.…”

Section: Introductionmentioning

confidence: 97%

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Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding

2010

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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.

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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding

2010

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“…In concept it is similar to the simulator used to prototype exclusion [11]. The authenticity of that simulator is borne out by subsequent real implementations of that technique.…”

Section: System Modelmentioning

confidence: 97%

Surprisal-aware scheduling of PTZ cameras

Detmold

Hengel

Dick

et al. 2009

2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC)

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An approach is presented for scheduling PTZ cameras on guard tours with two or more fields of view. In contrast to the target tracking of previous work, this approach seeks to optimise the coverage of the area under surveillance. Specifically, the aim is to minimise the surprisal (self-information) of events in unobserved fields of view. An entropy driven scheduler based on Kullback-Leibler divergence (information gain) is presented, and compared with three naive schedulers (random, round robin and constant selection of one field of view).Experiments investigate its performance on networks of ten cameras. These are evaluated over factors including four different scheduling approaches, different numbers of fields of view, and different inactive times whilst switching views. They demonstrate the efficacy of the entropy driven scheduler as it outperforms the naive schedulers by a significant margin by favouring certain fields of view that are more likely to reveal events with high surprisal value. The scheduler is target agnostic, as it operates on low level properties of the video signal, specifically, occupancy as determined by background subtraction. This permits an efficient implementation that is independent of the number of targets in the area under surveillance. As each camera is scheduled independently, the approach is scalable via distributed implementation, including on smart cameras.

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“…ASAP [10] provides scalable resource management by using application-specific prioritization cues. Hengel et al [11] approach scalability by partitioning the system according to an activity topology describing the observed (past) behavior of target objects in the network. OASIS provides a service-oriented programming framework for composing an application as a graph of modular services.…”

Section: D H I S T = C a L C H I S T ( Img Nb ) ; T R A C K E Rmentioning

confidence: 99%

“…Other projects that are related to TC include ASAP [10]; the activity topology design based surveillance middleware [11]; service-oriented architectures for sensor networks such as OASIS [12]; and the high level abstractions for sensor network programming [13], [14]. ASAP [10] provides scalable resource management by using application-specific prioritization cues.…”

Section: D H I S T = C a L C H I S T ( Img Nb ) ; T R A C K E Rmentioning

confidence: 99%

Target container: A target-centric parallel programming abstraction for video-based surveillance

Hong

Smaldone

Shin

et al. 2011

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras

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Abstract-We introduce a novel abstraction, the target container (TC), which serves as a parallel programming model and execution framework for developing complex applications for tracking multiple targets in a large-scale camera network. The key insight is to allow the domain expert (e.g., a vision researcher) to focus on the algorithmic details of target tracking and let the system deal with providing the computational resources (cameras, networking, and processing) to enable target tracking. Each TC has a one-to-one correspondence with a target, possibly tracked from multiple cameras. The domain expert provides the code modules for target tracking (such as detectors and trackers) as handlers to the TC system. The handlers are invoked dynamically by the TC system to discover new targets (detector) and to follow existing targets (tracker). The TC system also provides an interface for merging TCs whenever they are determined to be corresponding to the same target.This paper presents the design of the TC system, details of an experimental prototype, and an example application to demonstrate the simplicity of using the TC programming model.

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Activity Topology Estimation for Large Networks of Cameras

Cited by 23 publications

References 8 publications

Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding

Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding

Surprisal-aware scheduling of PTZ cameras

Target container: A target-centric parallel programming abstraction for video-based surveillance

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