Dynamical information fusion of heterogeneous sensors for 3D tracking using particle swarm optimization

Kirchmaier, Ulrich; Hawe, Simon; Diepold, Klaus

doi:10.1016/j.inffus.2010.06.005

Cited by 15 publications

(13 citation statements)

References 17 publications

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“…Tracking and localization of human objects is a task of increasing interest and of great relativity to the DESMOS framework. Human localization is quite complex, and its difficulty grows when the task is performed in indoor spaces, due to the existence of crowd and obstacles [21]. Fusion of multiple sensor data in object localization studies is usually performed by Kalman filtering.…”

Section: Related Workmentioning

confidence: 99%

“…Its simplicity creates the advantage of speed, but the underlying distribution assumptions cause limitations [22]. In [21], a Kalman filter data fusion technique is proposed that combines sensors embedded in a wearable platform, namely accelerometer, gyroscope and magnetometer, for indoor localization and position tracking. Kalman based fusion is also applied in [22] to combine audio and visual data from heterogeneous sensors for object tracking.…”

Section: Related Workmentioning

confidence: 99%

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Internet of Things Infrastructure for Security and Safety in Public Places

et al. 2019

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We present the technologies and the theoretical background of an intelligent interconnected infrastructure for public security and safety. The innovation of the framework lies in the intelligent combination of devices and human information towards human and situational awareness, so as to provide a protection and security environment for citizens. The framework is currently being used to support visitors in public spaces and events, by creating the appropriate infrastructure to address a set of urgent situations, such as health-related problems and missing children in overcrowded environments, supporting smart links between humans and entities on the basis of goals, and adapting device operation to comply with human objectives, profiles, and privacy. State-of-the-art technologies in the domain of IoT data collection and analytics are combined with localization techniques, ontologies, reasoning mechanisms, and data aggregation in order to acquire a better understanding of the ongoing situation and inform the necessary people and devices to act accordingly. Finally, we present the first results of people localization and the platforms’ ontology and representation framework.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Internet of Things Infrastructure for Security and Safety in Public Places

et al. 2019

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“…While SI-based algorithms mostly descend from the field of optimization, they were also successfully applied to computer vision and image processing tasks, ranging from edge detection [25], over feature extraction and object recognition [26,27], to three-dimensional tracking in stereo-video [28]. All these algorithms offer robustness alongside low computational costs.…”

Section: Principles Of Swarm Intelligencementioning

confidence: 99%

A Swarm Intelligence inspired algorithm for contour detection in images

Kirchmaier

Hawe

Diepold

2013

Applied Soft Computing

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Swarm Intelligence uses a set of agents which are able to move and gather local information in a search space and utilize communication, limited memory, and intelligence for problem solving. In this work, we present an agent-based algorithm which is specifically tailored to detect contours in images. Following a novel movement and communication scheme, the agents are able to position themselves distributed over the entire image to cover all important image positions. To generate global contours, the agents examine the local windowed image information, and based on a set of fitness functions and via communicating with each other, they establish connections. Instead of a centralized paradigm, the global solution is discovered by some principal rules each agent is following. The algorithm is independent of object models or training steps. In our evaluation we focus on boundary detection as a major step towards image segmentation. We therefore evaluate our algorithm using the Berkeley Segmentation Dataset (BSDS) and compare its performance to existing methods via the BSDS benchmark and Pratt's Figure of Merit.

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“…Locating a target in 3D offers important information to analyse the target as well as the interactions between the robot and the environment. However, only a few works have addressed the problem of tracking a speaker in 3D using a localised sensor platform [7] [8].…”

Section: Introductionmentioning

confidence: 99%

3D audio-visual speaker tracking with an adaptive particle filter

Qian

Brutti

Omologo

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose an audiovisual fusion algorithm for 3D speaker tracking from a localised multi-modal sensor platform composed of a camera and a small microphone array. After extracting audiovisual cues from individual modalities we fuse them adaptively using their reliability in a particle filter framework. The reliability of the audio signal is measured based on the maximum Global Coherence Field (GCF) peak value at each frame. The visual reliability is based on colour-histogram matching with detection results compared with a reference image in the RGB space. Experiments on the AV16.3 dataset show that the proposed adaptive audiovisual tracker outperforms both the individual modalities and a classical approach with fixed parameters in terms of tracking accuracy.

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Dynamical information fusion of heterogeneous sensors for 3D tracking using particle swarm optimization

Cited by 15 publications

References 17 publications

Internet of Things Infrastructure for Security and Safety in Public Places

Internet of Things Infrastructure for Security and Safety in Public Places

A Swarm Intelligence inspired algorithm for contour detection in images

3D audio-visual speaker tracking with an adaptive particle filter

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