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Energy-efficient face detection and recognition scheme for wireless visual sensor networks

APPLIED SOFT COMPUTING 2020年第0期89卷 106014-000页

作者： Zam, Abdulaziz Khayyambashi, Mohammad Reza Bohlooli, Ali Univ Isfahan Fac Comp Engn Hezar Jerib Ave Esfahan *** Iran

Energy-efficient and robust face detection and recognition scheme can be useful for many application fields such as security and surveillance in multimedia and visual sensor network (VSN). VSN consists of wireless resources-constrained nodes that are equipped with low-energy CMOS cameras for monitoring. On the one hand, captured images are meaningful multimedia-data that impose high energy consumption to be processed and transmitted. On the other hand, visual sensor (VS) is a battery-powered node with limited life-time. This situation leads to a trade-off between detection-accuracy and power-consumption. This trade-off is considered as the most major challenge for applications using multimedia data in wireless environments such as VSN. For optimizing this trade-off, a novel face detection and recognition scheme has been proposed in this paper based on VSN. In this scheme, detection phase is performed at VS and recognition phase is accomplished at the base station (sink). The contributions of this paper are in three folds: 1. Fast and energy-aware face-detection algorithm is proposed based on omitting non-human blobs and feature-based face detection in the considered human-blobs. 2. A novel energy-aware and secure algorithm for extracting light-weight discriminative vector of detected face-sequence to be sent to sink with low transmission-cost and high security level. 3. An efficient face recognition algorithm has been performed on the received vectors at the sink. The performance of our proposed scheme has been evaluated in terms of energy-consumption, detection and recognition accuracy. Experimental results, performed on standard datasets (FERET, Yale and CDnet) and on personal datasets, demonstrate the superiority of our scheme over the recent state-of-the-art methods. (C) 2019 Published by Elsevier B.V.

关键词： visual sensor network Detection-accuracy and energy-consumption trade-off Energy efficient face detection Distinctive vector Face recognition

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Saliency-based bit plane detection for network applications

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第25-26期79卷 18495-18513页

作者： Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Hakak, Saqib Idris, Mohd Yamani Idna Anisi, Mohammad Hossein Rajan, Deepu Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Univ Northern British Columbia Fac Comp Sci Prince George BC Canada Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

Transmitting image data without losing significant information is challenging for any network application especially when large color images are transmitted through TCP communication protocol. This is due to network limitations such as buffer overflow, underflow and network traffic flow etc. This paper presents a new method for image size reduction such that the network can transmit data without much loss of information, and hence, quality. The proposed method obtains bit planes for the color input images, which results in eight binary planes. Unlike the existing bit plane based image size reduction methods, which assume that the most significant plane or some other planes contain useful information, the proposed method finds the plane that contains dominant information automatically. For each plane, the proposed method explores the saliency that finds dominant information based on Markov Chain Process and similarity estimation between neighbor pixels. To reduce computational burden, we use Canny edge maps of the saliency of the planes for feature extraction. We propose to explore ring-growing concept for the edge maps to study the spatial distribution of saliency, locally. The proposed method detects the plane based on statistics of saliency distribution. To validate the step of plane detection, we estimate transmission error caused during data transmission through TCP communication protocol for the images at sending and receiving ends. Experimental results on plane detection show that the proposed method is better than the existing methods in terms of detection rate. Our experiments on image transmission through TCP communication protocol show that the proposed method outperforms the existing methods in terms of error estimation and quality analysis. Furthermore, experiments are conducted to analyze packet loss in terms of number of duplicate acknowledgements and retransmission during packets transmission for the color, edge and plane to show that transmitting pla

关键词： Bit plane detection Canny edge image Saliency map Markov chain process visual sensor network TCP protocol

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Exploring Contextual Redundancy in Improving Object-Based Video Coding for Video sensor networks Surveillance

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IEEE TRANSACTIONS ON MULTIMEDIA 2012年第3期14卷 669-682页

作者： Tsai, Tsung-Han Lin, Chung-Yuan Natl Cent Univ Dept Elect Engn Jhongli 3201 Taoyuan Taiwan

In recent years, intelligent video surveillance attempts to provide content analysis tools to understand and predict the actions via video sensor networks (VSN) for automated wide-area surveillance. In this emerging network, visual object data is transmitted through different devices to adapt to the needs of the specific content analysis task. Therefore, they raise a new challenge for video delivery: how to efficiently transmit visual object data to various devices such as storage device, content analysis server, and remote client server through the network. Object-based video encoder can be used to reduce transmission bandwidth with minor quality loss. However, the involved motion-compensated technique often leads to high computational complexity and consequently increases the cost of VSN. In this paper, contextual redundancy associated with background and foreground objects in a scene is explored. A scene analysis method is proposed to classify macroblocks (MBs) by type of contextual redundancy. The motion search is only performed on the specific type of context of MB which really involves salient motion. To facilitate the encoding by context of MB, an improved object-based coding architecture, namely dual-closed-loop encoder, is derived. It encodes the classified context of MB in an operational rate-distortion-optimized sense. The experimental results show that the proposed coding framework can achieve higher coding efficiency than MPEG-4 coding and related object-based coding approaches, while significantly reducing coding complexity.

关键词： Contextual redundancy coding intelligent video surveillance object-based video coding operational rate-distortion theory visual sensor network

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Discovering activity patterns in office environment using a network of low-resolution visual sensors

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2018年第2期9卷 381-411页

作者： Eldib, Mohamed Deboeverie, Francis Philips, Wilfried Aghajan, Hamid Univ Ghent IMEC TELIN Image Proc & Interpretat Sint Pietersnieuwstr 41 B-9000 Ghent Belgium Ambient Intelligence Res Lab David Packard Bldg Stanford CA 94305 USA

Understanding activity patterns in office environments is important in order to increase workers' comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the users' locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users' mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individual's tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the user's presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the user's status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the persons' daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire group's activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events.

关键词： visual sensor network Supervised learning Probabilistic graphical models Topic models Sequence mining

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Energy Efficient Routing Mechanism Based on Similarity Guided Graph Neural network and Decision Tree with Golden Eagle Optimization in VSN

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PEER-TO-PEER networkING AND APPLICATIONS 2024年第5期17卷 3408-3423页

作者： Rajkumar, K. Paramasivan, B. Natl Engn Coll Dept CSE Kovilpatti 628503 Tamil Nadu India Natl Engn Coll Dept Informat Technol Kovilpatti 628503 Tamilnadu India

Recent developments in low power sensors have prompted the creation of visual sensor network (VSN). Coverage, availability, network life duration, and energy usage are important issues that arise in VSN. However several energy-efficient protocols have been developed, but those protocols have transmission collisions and energy loss due to increased data redundancy. In order to overcome these challenges, an Energy Efficient Routing based Sleep Scheduling Mechanism (EERSSM) is developed. Camera nodes are randomly deployed in the visual sensor network, and the data is received from the network through a relay node. Energy, distance, and node stability are taken into account while identifying the relay node. The next step is to use a Similarity Graph guided Neural network (SGGNN) to determine whether neighboring nodes detect similar data. If similar information is detected, a similarity measure is computed. When the value of the similarity measure exceeds the threshold, the node goes into the sleep stage while the other nodes are in the wakeup stage. A decision tree is used to calculate the sleep cycle depending on a few factors. The decision tree has a number of hyperparameters, and those parameters are tuned using Golden Eagle Optimization (GEO). When the update cycle is over, the node awakens and joins in the transmission procedure. This proposed energy efficient routing algorithm is tested with several metrics that attain better performance, like 14.42 J average residual energy, 93% packet delivery ratio, 9.3% throughput value, and 770 s network lifetime. Thus, the techniques used in the proposed approach are the better choice for solving the availability, energy consumption, and network lifetime issues in VSN.

关键词： visual sensor network Node stability Sleep wakeup approach Similarity graph guided neural network Golden eagle optimization Decision tree

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Multiple-Object Tracking in Large-Scale Scene

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2016年第7期E99D卷 1903-1909页

作者： Yuan, Wenbo Cao, Zhiqiang Tan, Min Chen, Hongkai Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China

In this paper, a multiple-object tracking approach in large-scale scene is proposed based on visual sensor network. Firstly, the object detection is carried out by extracting the HOG features. Then, object tracking is performed based on an improved particle filter method. On the one hand, a kind of temporal and spatial dynamic model is designed to improve the tracking precision. On the other hand, the cumulative error generated from evaluating particles is eliminated through an appearance model. In addition, losses of the tracking will be incurred for several reasons, such as occlusion, scene switching and leaving. When the object is in the scene under monitoring by visual sensor network again, object tracking will continue through object re-identification. Finally, continuous multiple-object tracking in large-scale scene is implemented. A database is established by collecting data through the visual sensor network. Then the performances of object tracking and object re-identification are tested. The effectiveness of the proposed multiple-object tracking approach is verified.

关键词： visual sensor network HOG improved particle filter re-identification object tracking

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Cooperative Estimation of Averaged 3-D Moving Target Poses via networked visual Motion Observer

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2013年第3期58卷 623-638页

作者： Hatanaka, Takeshi Fujita, Masayuki Tokyo Inst Technol Dept Mech & Control Engn Tokyo 1528550 Japan

This paper investigates cooperative estimation of averaged moving target object poses in three dimensions for visual sensor networks. In particular, we consider the situation where multiple vision cameras see a common target object but the poses consistent with visual measurements differ fromcamera to camera due to a variety of uncertainties. Under the situation, we try to estimate an average of the contaminated poses not only for static but also for moving target objects by using only local negotiations. For this purpose, we present a cooperative estimationmechanism called networked visualmotion observer. We then derive an upper bound of the ultimate error between the actual average and the estimates produced by the present estimation mechanism for both static and moving target objects. Finally the effectiveness of the networked visual motion observer is demonstrated through simulation.

关键词： Averaging cooperative estimation vision-based pose estimation visual sensor network

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Energy efficient processing of motion estimation for embedded multimedia systems

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第23期76卷 24749-24765页

作者： Lee, Jooheung Hongik Univ Dept Elect & Elect Engn Sejong South Korea

visual sensor networks require low power compression techniques of large amount of video data in each camera node due to the energy-constrained and bandwidth-limited environments. In this paper, energy-efficient architecture for Variable Block Size Motion Estimation is proposed to fully utilize dynamic partial reconfiguration capability of programmable hardware fabric in distributed embedded vision processing nodes. Partial reconfiguration of FPGA is exploited to support run-time reconfiguration of the proposed modular hardware architecture for motion estimation. According to the required search range, hardware reconfiguration is performed adaptively to reduce the hardware resources and power consumption. A reconfigurable ME ranging from simple 1-D to a complex 2-D Sum of Absolute Differences (SAD) array to perform full search block matching is selected in order to support different search window size. The implemented scalable SAD array can provide different resolutions and frame rates for real time applications with multiple reconfigurable regions.

关键词： Partial reconfiguration Variable block size motion estimation Reconfigurable computing visual sensor network

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Multiple RGB-D sensor-based 3-D reconstruction and localization of indoor environment for mini MAV

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 70卷 509-524页

作者： Li, Yamin Wang, Yong Wang, Dianhong China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Hubei Peoples R China

Micro aerial vehicle (MAV) with lower cost, smaller size and more flexible flight performance plays a greater role in the global position system-denied (GPS-denied) indoor tasks. This paper focuses on the realization of three-dimensional (3-D) panoramic environment reconstruction and target localization without relying on any external navigation aid. By establishing a visual sensor network (VSN) composed of multiple RGB-Depth (RGB-D) sensors, a fast 3-D model of the observed environment is built utilizing an improved speeded up robust feature (SURF) extraction algorithm and iterative closest point-based (ICP-based) reconstruction algorithm. A distributed data fusion algorithm known as kalman-consensus filter (KCF) is then used to estimate a more accurate global position and trajectory of mini MAV. Both software simulations and real indoor flight experiment results demonstrate that the proposed approach achieves a fast and reliable 3-D map of the indoor environment and the accuracy of localization is largely improved. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Mini MAV visual sensor network 3-D reconstruction Indoor localization Kalman-consensus filter Data fusion

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Modeling and Maximizing Angle Coverage in visual sensor networks

Modeling and Maximizing Angle Coverage in Visual Sensor Netw...

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第37届中国控制会议

作者： Yonghua Xiong Manjie Lu Wei Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Guizhou Tobacco Science Institute

Maintaining the quality of network coverage is a major concern in visual sensor networks. In this paper, we study the angle coverage problem in visual sensor networks, considering the target is very large, and each camera node can only monitor a portion of its perimeter. The goal of the proposed work is to schedule camera nodes to achieve maximum angle coverage in different period. Firstly, we establish a novel coverage model and formally prove that the problem is NP-hard in ***, we present a scheduling scheme based on greedy algorithm, to schedule camera nodes into disjoint cover sets working in ***, we conclude that the approximate ratio of the proposed algorithm is k, theoretically, and the time complexity of the algorithm is O（n）. Finally, extensive simulations have been conducted to evaluate the performance of the proposed algorithm.

关键词： visual sensor network angle coverage camera node scheduling greedy algorithm

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