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Feature compression: A framework for multi-view multi-person tracking in visual sensor networks

JOURNAL OF visual COMMUNICATION AND IMAGE REPRESENTATION 2014年第5期25卷 864-873页

作者： Cosar, Serhan Cetin, Mujdat Sabanci Univ Fac Engn & Nat Sci TR-34956 Tuzla Istanbul Turkey

visual sensor networks (VSNs) consist of image sensors, embedded processors and wireless transceivers which are powered by batteries. Since the energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem. In this paper, we present a framework for human tracking in VSNs. The traditional approach of sending compressed images to a central node has certain disadvantages such as decreasing the performance of further processing (i.e., tracking) because of low quality images. Instead, we propose a feature compression-based decentralized tracking framework that is better matched with the further inference goal of tracking. In our method, each camera performs feature extraction and obtains likelihood functions. By transforming to an appropriate domain and taking only the significant coefficients, these likelihood functions are compressed and this new representation is sent to the fusion node. As a result, this allows us to reduce the communication in the network without significantly affecting the tracking performance. An appropriate domain is selected by performing a comparison between well-known transforms. We have applied our method for indoor people tracking and demonstrated the superiority of our system over the traditional approach and a decentralized approach that uses Kalman filter. (C) 2014 Elsevier Inc. All rights reserved.

关键词： visual sensor networks Camera networks Human tracking Decentralized tracking Communication constraints Feature compression Compressing likelihood functions Bandwidth-efficient tracking

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Low Complexity HEVC Encoder for visual sensor networks

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sensorS 2015年第12期15卷 30115-30125页

作者： Pan, Zhaoqing Chen, Liming Sun, Xingming Nanjing Univ Informat Sci & Technol Sch Comp & Software Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Jiangsu Peoples R China Hebei Univ Technol Sch Comp Sci & Engn Tianjin 300401 Peoples R China

visual sensor networks (VSNs) can be widely applied in security surveillance, environmental monitoring, smart rooms, etc. However, with the increased number of camera nodes in VSNs, the volume of the visual information data increases significantly, which becomes a challenge for storage, processing and transmitting the visual data. The state-of-the-art video compression standard, high efficiency video coding (HEVC), can effectively compress the raw visual data, while the higher compression rate comes at the cost of heavy computational complexity. Hence, reducing the encoding complexity becomes vital for the HEVC encoder to be used in VSNs. In this paper, we propose a fast coding unit (CU) depth decision method to reduce the encoding complexity of the HEVC encoder for VSNs. Firstly, the content property of the CU is analyzed. Then, an early CU depth decision method and a low complexity distortion calculation method are proposed for the CUs with homogenous content. Experimental results show that the proposed method achieves 71.91% on average encoding time savings for the HEVC encoder for VSNs.

关键词： visual sensor networks video compression HEVC coding unit low complexity

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Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in visual sensor networks

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sensorS 2014年第12期14卷 22408-22430页

作者： Yang, Yong Tong, Song Huang, Shuying Lin, Pan Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330032 Peoples R China Jiangxi Univ Finance & Econ Sch Software & Commun Engn Nanchang 330032 Peoples R China Xi An Jiao Tong Univ Inst Biomed Engn Educ Minist Key Lab Biomed Informat Engn Xian 710049 Peoples R China

This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low-and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs.

关键词： multi-focus image fusion dual-tree complex wavelet transform image block residual visual sensor networks

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Coverage optimization of visual sensor networks for observing 3-D objects: survey and comparison

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2019年第4期3卷 342-361页

作者： Zhang, Xuebo Zhang, Boyu Chen, Xiang Fang, Yongchun Nankai Univ Inst Robot & Automat Informat Syst Tianjin 300071 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

Coverage is a basic and critical issue for design and deployment of visual sensor networks, however, the optimization problem is very challenging especially when considering coverage of three-dimensional (3-D) scenarios. This paper provides a brief survey of mainstream coverage optimization methods for visual sensor networks, including the greedy algorithm, genetic algorithm (GA), particle swarm optimization (PSO), binary integer programming (BIP) and differential evolution algorithm (DE). We provide an efficient open-source C++ implementation of these algorithms and compare their performance on a typical camera deployment problem for coverage of 3-D objects. In order to improve the computational efficiency, a parallel visual occlusion detection approach is proposed and implemented with graphic processing units (GPUs), which are then integrated into all of the aforementioned optimization approaches for a fair comparison. Evaluation results show that (1) the proposed parallel occlusion detection algorithm largely improves the computational efficiency;(2) among the five typical approaches, BIP has the best coverage performance yet with the highest time cost, and greedy algorithm is the fastest approach at the price of coverage performance;GA, PSO, and DE achieve a compromise between the performance and the time cost, while DE has better coverage performance and less time cost than PSO and GA. These results could serve as engineering guidelines and baselines for further improvement of coverage optimization algorithms.

关键词： visual sensor networks Coverage Optimization Parallel occlusion detection

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On optimizing the deployment cost of visual sensor networks

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JOURNAL OF HIGH SPEED networks 2017年第1期24卷 17-30页

作者： Mehdi, Rouan-Serik Mejdi, Kaddour Univ Oran 1 Lab LITIO Ahmed Ben Bella Algeria

We study in this paper the problem of minimizing the number and the locations of deployed cameras in visual sensor networks where to objective is to monitor a set of targets with a predefined quality level. To this end, we first propose a mathematical programming formulation, based on mixed-integer linear programming (MILP), which is designed to provide optimal solutions in case where the deployment area is represented through a set of discrete potential locations of the cameras. Due to the combinatorial nature of such problems, finding exact solutions entails a tremendous computational cost. Consequently, we introduce various suboptimal solution approaches, based on a number of well-known metaheuristics, such as particle swarm optimization (PSO) and genetic algorithms. Numerical results show PSO succeeds to find the best solutions in the majority of considered scenarios. Furthermore, even for large instances, it provides better feasible solutions than those returned the MILPs after a significant amount of time.

关键词： visual sensor networks deployment problem mixed-integer linear programming particle swarm optimization genetic algorithms greedy algorithms simulated annealing target coverage

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Utility-based Camera Node Assignment for Pedestrian Tracking in visual sensor networks 8

Utility-based Camera Node Assignment for Pedestrian Tracking...

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8th International Conference on Intelligent Computation Technology and Automation (ICICTA)

作者： Tan Yuqi Wang Xue Tsinghua Univ State Key Lab Precis Measurement Technol & Instru Dept Precis Instrument Beijing 100084 Peoples R China

ISBN: (纸本)9781467376440

Camera assignment is of great importance for pedestrian tracking in visual sensor networks, since it can not only provide best view for tracking but also save communication resources of the networks. This paper proposes a camera assignment method based on fuzzy logic. First, three criteria related to pedestrian's size, position, view on the image are designed to evaluate the information of the camera nodes. Second, the scores for these three criteria are fused to the value of suitability by proposed fuzzy inference engine. At last, combing with the latest variation of the criteria scores, the camera nodes with highest score are selected for pedestrian tracking. Our method is validated in visual sensor network including eight camera nodes. The experiment results show the superiority of our method over linear combination approach.

关键词： Camera Assignment visual sensor networks Fuzzy Inference Engine Pedestrian Tracking

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Prediction-based Object Tracking and Coverage in visual sensor networks

Prediction-based Object Tracking and Coverage in Visual Sens...

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7th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Chen, Tzung-Shi Peng, Jiun-Jie Lee, De-Wei Tsai, Hua-Wen Natl Univ Tainan Dept Com Sci & Info Engn Tainan 700 Taiwan Natl Univ Tainan Dept Info & Learning Technol Tainan 700 Taiwan Lunghwa Univ Sci & Technol Dept Comp Info & Network Eng Taoyuan Taiwan

ISBN: (纸本)9781424495375

This paper is to study the tracking of mobile objects and associated coverage problems in visual sensor networks. When sensors equipped with cameras are randomly deployed in a monitoring environment, many cameras are involved in covering the same mobile object. Images of the object may be captured by different camera nodes in different orientations simultaneously, and the captured images are then sent back to a base station or server. However, achieving characteristic point coverage of objects invariably involves a great deal of redundant image data consuming the transmission energy of the visual sensor network. One approach is proposed to overcome this problem. The minimum number of camera nodes required for set coverage can be determined by predicting the direction and speed of the mobile object. The simulation results show that this protocol reduces transmission cost while preserving the maximum coverage range of mobile objects.

关键词： camera node coverage object tracking prediction visual sensor networks

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Self-organising Zooms for Decentralised Redundancy Management in visual sensor networks 9

Self-organising Zooms for Decentralised Redundancy Managemen...

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9th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

作者： Esterle, Lukas Rinner, Bernhard Lewis, Peter R. Alpen Adria Univ Klagenfurt LakesideLabs Klagenfurt Inst Networked & Embedded Syst Klagenfurt Austria Aston Univ Syst Analyt Res Inst Aston Lab Intelligent Collect Engn ALICE Birmingham B4 7ET W Midlands England

ISBN: (纸本)9781467375351

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras' fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator's preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator's preferences.

关键词： visual sensor networks redundancy management decentralised learning self-organisation runtime trade-offs

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Robot tracking in low-power visual sensor networks 16

Robot tracking in low-power visual sensor networks

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10th International Conference on Distributed Smart Cameras (ICDSC)

作者： Olti, Emoke Verbeke, Thomas Braeckman, Geert Dadarlat, Vasile Teodor Munteanu, Adrian TUCN Cluj Napoca Romania VUB Brussels Belgium

ISBN: (纸本)9781450347860

The paper addresses the problem of visual tracking in low-power visual sensor networks. Accurate real-time localization and energy efficiency are key issues in such applications. A novel approach for tracking and identification of robots using 1D barcodes is proposed. The nodes in the network perform visual processing and export processed information in form of robot codes and motion vectors. The central server can turn on/off processing on either node based on the received information. Furthermore, a novel LoRa-based low-power communication protocol is designed to provide the necessary bandwidth. The proposed system yields optimized node energy consumption, which we model mathematically and compare against the energy consumption of a system of independent cameras. The experimental results prove the correctness of the formulated model and demonstrate the advantages of the proposed approach. The performance of the system is practically demonstrated on LoRa-enabled Raspberry Pi cameras.

关键词： visual sensor networks Robot tracking LoRa networks

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Loss less Compression of Binary Image Descriptors for visual sensor networks

Loss less Compression of Binary Image Descriptors for Visual...

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18th International Conference on Digital Signal Processing (DSP)

作者： Ascenso, Joao Pereira, Fernando Inst Super Engn Lisboa Inst Telecomun Lisbon Portugal Inst Super Tecn Inst Telecommun Lisbon Portugal

ISBN: (纸本)9781467358057

Nowadays, visual sensor networks have emerged as an important research area for distributed signal processing, with unique challenges in terms of performance, complexity, and resource allocation. In visual sensor networks, the energy consumption must be kept low to extend the lifetime of each battery-operated camera node. Thus, considering the large amount of data that visual sensors can generate, all the sensing, processing, and transmission operations must be optimized considering strict energy constraints. In this paper, camera nodes sense the visual scene but instead of transmitting the pixel coded representation, which demands high computation and bandwidth, a compact but yet rich visual representation is created and transmitted. This representation consists of discriminative visual features offering tremendous potential for several image analysis tasks. From all low-level image features available, the novel class of binary features, very fast to compute and match, are well suited for visual sensor networks. In this paper, lossless compression of binary image features is proposed to further lower the energy and bandwidth requirements. The coding solution exploits the redundancy between descriptors of an image by sorting the descriptors and applying DPCM and arithmetic coding. Experimental results show improvements up to 32% in terms of bitrate savings without any impact in the final image retrieval task accuracy.

关键词： visual sensor networks feature coding binary features image retrieval

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