Distributed Smart Cameras and Distributed Computer Vision

Wolf, Marilyn; Schlessman, Jason

doi:10.1007/978-3-319-91734-4_10

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Many distributed methods can obtain good tracking accuracy with the observations of each node in sensor networks [2][3][4], but they cannot handle the scenario shown in Fig. 1, where each node can only observe a small part of the target trajectory.…”

Section: Description and Solution Of The Problemmentioning

confidence: 99%

Distributed Color-Based Particle Filter for Target Tracking in Camera Network

Jing

Chen

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Color-based particle filters have appeared in some literatures. However, there are still some important drawback in tracking targets, such as illumination changes, occlusion and low tracking accuracy.To solve these problems, in this paper, we propose a distributed colorbased particle filter (DCPF) for target tracking, which can track targets accurately in a large-scale camera network with less data transmission and less computation. Compared with the previous algorithms, the algorithm proposed in this paper has two obvious advantages. First, the DCPF framework merges color features into the target's state to obtain better robustness. Second, it considers the situation where the target is disappear in some cameras because of limited field of view (FoV). Convincing results are confirmed that the performance analysis of the proposed algorithm in this paper is very close to the centralized particle filter method.

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Section: Description and Solution Of The Problemmentioning

confidence: 99%

Distributed Color-Based Particle Filter for Target Tracking in Camera Network

Jing

Chen

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Data-Driven Logical Topology Inference for Managing Safety and Re-Identification of Patients Through Multi-Cameras IoT

et al. 2019

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As Internet of Things (IoT) develops, IoT technologies are starting to integrate intelligent cameras for managing safety within mental health hospital wards and relevant spaces, seeking out specified individuals from these surveillance videos filmed by the various cameras. Because monitoring is one of the important application of IoT based on distributed video cameras. In order to fine-grained re-identification of patients and their activities against the very low resolution, occlusions and pose, viewpoint and illumination changes, we propose a novel data-driven model to infer multi-cameras logical topology and re-identify patients captured by different cameras. In our model, we employ a Time-Delayed Mutual Information (TDMI) model in order to address multi-cameras logical topology inference. Additionally, we use a welltrained Deep Convolutional Neural Network (DCNN) to extract characteristics. Moreover, we employ a name-ability model to discover deep attributes and a classifier based on a structural output of attributes is designed to tackle the re-identification of patients, especially who possess psychiatric behaviour. In order to improve the present model's performance, we resort to the parallelized implementations. Experimental results show that our model possesses the best performance as compared to state-of-the-art model,especially, when the semantic restrictions are imposed onto the production of patients' specific attributes with structural output. Further, the deep learning model is used to produce characteristics when there is no supervision on the learning model of attributes. INDEX TERMS Canonical correlation analysis, time delayed mutual information (TDMI), deep convolutional neural network (DCNN), multi-camera topology inference.

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Distributed Smart Cameras and Distributed Computer Vision

Cited by 2 publications

References 27 publications

Distributed Color-Based Particle Filter for Target Tracking in Camera Network

Distributed Color-Based Particle Filter for Target Tracking in Camera Network

Data-Driven Logical Topology Inference for Managing Safety and Re-Identification of Patients Through Multi-Cameras IoT

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