distributed object recognition is a significantly fast-growing research area, mainly motivated by the emergence of high performance cameras and their integration with modern wireless sensor network technologies. In wi...
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ISBN:
(纸本)9781467399616
distributed object recognition is a significantly fast-growing research area, mainly motivated by the emergence of high performance cameras and their integration with modern wireless sensor network technologies. In wireless distributed object recognition, the bandwidth is limited and it is desirable to avoid transmitting redundant visual features from multiple cameras to the base station. In this paper, we propose a histogram compression and feature selection framework based on Sparse Non-negative Matrix Factorization (SNMF). In our proposed method, histograms of the features are modeled as linear combination of a small set of signature vectors with associated weight vectors. The recognition process in the base station is then performed based on these small sets of transmitted weights from each camera. Furthermore, we propose another novel distributed object recognition scheme based on local classification in each camera and sending the label information to the base station and making the final decision based on majority voting. Experiments on BMW dataset affirm that our approach outperforms the state of the art in accuracy and bandwidth usage.
In the distributed object recognition problem at least two robots are placed randomly in an unknown environment. The robots have to identify the same object in the environment. We describe a solution to distributed ob...
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ISBN:
(纸本)0819434329
In the distributed object recognition problem at least two robots are placed randomly in an unknown environment. The robots have to identify the same object in the environment. We describe a solution to distributed object recognition that computes the transformation of coordinates between two robots' local coordinate frames. This transformation is then used as a translator between the robots' images. We present experimental results from an implementation of this algorithm.
distributed object recognition is a significantly fast-growing research area, mainly motivated by the emergence of high performance cameras and their integration with modern wireless sensor network technologies. In wi...
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ISBN:
(纸本)9781467399623
distributed object recognition is a significantly fast-growing research area, mainly motivated by the emergence of high performance cameras and their integration with modern wireless sensor network technologies. In wireless distributed object recognition, the bandwidth is limited and it is desirable to avoid transmitting redundant visual features from multiple cameras to the base station. In this paper, we propose a histogram compression and feature selection framework based on Sparse Non-negative Matrix Factorization (SNMF). In our proposed method, histograms of the features are modeled as linear combination of a small set of signature vectors with associated weight vectors. The recognition process in the base station is then performed based on these small sets of transmitted weights from each camera. Furthermore, we propose another novel distributed object recognition scheme based on local classification in each camera and sending the label information to the base station and making the final decision based on majority voting. Experiments on BMW dataset affirm that our approach outperforms the state of the art in accuracy and bandwidth usage.
Visual surveillance in complex urban environments requires an intelligent system to automatically track and identify multiple objects of interest in a network of distributed cameras. The ability to perform robust obje...
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ISBN:
(纸本)9780982443804
Visual surveillance in complex urban environments requires an intelligent system to automatically track and identify multiple objects of interest in a network of distributed cameras. The ability to perform robust objectrecognition is critical to compensate adverse conditions and improve performance, such as multi-object association, visual occlusion, and data fusion with hybrid sensor modalities. In this paper we propose an efficient distributed data compression and fusion scheme to encode and transmit SIFT-based visual histograms in a multi-hop network to perform accurate 3-D objectrecognition. The method harnesses an emerging theory of (distributed) compressive sensing to encode high-dimensional, nonnegative sparse signals via random projection, which is unsupervised and independent to the sensor modality. A multi-hop protocol then transmits the compressed visual data to a base-station computer which preserves a constant bandwidth regardless of the number of active camera nodes in the network. Finally, the multiple-view object features are simultaneously recovered via l(1)-minimization as an efficient decoder The efficacy of the algorithm is validated using up to four Berkeley CITRIC camera motes deployed in a realistic indoor environment. The substantial computation power on the CITRIC mote also enables fast compression of SIFT-ope visual features extracted from object images.
In this paper, we study the classical problem of objectrecognition in low-power, low-bandwidth distributed camera networks. The ability to perform robust objectrecognition is crucial for applications such as visual ...
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ISBN:
(纸本)9781424446193
In this paper, we study the classical problem of objectrecognition in low-power, low-bandwidth distributed camera networks. The ability to perform robust objectrecognition is crucial for applications such as visual surveillance to track and identify objects of interest, and compensate visual nuisances such as occlusion and pose variation between multiple camera views. We propose an effective framework to perform distributed object recognition using a network of smart cameras and a computer as the base station. Due to the limited bandwidth between the cameras and the computer, the method utilizes the available computational power on the smart sensors to locally extract and compress SIFT-type image features to represent individual camera views. In particular, we show that between a network of cameras, high-dimensional SIFT histograms share a joint sparse pattern corresponding to a set of common features in 3-D. Such joint sparse patterns can be explicitly exploited to accurately encode the distributed signal via random projection, which is unsupervised and independent to the sensor modality. On the base station, we study multiple decoding schemes to simultaneously recover the multiple-view object features based on the distributed compressive sensing theory. The system has been implemented on the Berkeley CITRIC smart camera platform. The efficacy of the algorithm is validated through extensive simulation and experiments.
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