Energy-efficient relay tracking with multiple mobile camera sensors

Wang, Tian; Zeng, Jiandian; Bhuiyan, Zakirul Alam; Chen, Yonghong; Cai, Yiqiao; Tian, Hui; Xie, Mei

doi:10.1016/j.comnet.2018.01.002

Cited by 16 publications

(5 citation statements)

References 25 publications

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“…Zheng Y et al proposed using topology structure to allocate multiple cameras for movingtarget tracking [11]. Wang T et al proposed a collaborative relay tracking algorithm based on multiple cameras, which used a genetic algorithm to shorten the total moving distance of cameras in the tracking process [12]. Zhang GF et al studied a multi-camera visual-positioning technology, which combined KNN-based background segmentation algorithm and a KCF algorithm with a feature-matching algorithm to achieve continuous and reliable target tracking.…”

Section: Related Workmentioning

confidence: 99%

Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach

Rao

Chen

et al. 2022

Sensors

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Cameras are widely used in the detection and tracking of moving targets. Compared to target visual tracking using a single camera, cooperative tracking based on multiple cameras has advantages including wider visual field, higher tracking reliability, higher precision of target positioning and higher possibility of multiple-target visual tracking. With vast ocean and sea surfaces, it is a challenge using multiple cameras to work together to achieve specific target tracking and detection, and it will have a wide range of application prospects. According to the characteristics of sea-surface moving targets and visual images, this study proposed and designed a sea-surface moving-target visual detection and tracking system with a multi-camera cooperation approach. In the system, the technologies of moving target detection, tracking, and matching are studied, and the strategy to coordinate multi-camera cooperation is proposed. The comprehensive experiments of cooperative sea-surface moving-target visual tracking show that the method used in this study has improved performance compared with contrapositive methods, and the proposed system can meet the needs of multi-camera cooperative visual tracking of moving targets on the sea surface.

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Section: Related Workmentioning

confidence: 99%

Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach

Rao

Chen

et al. 2022

Sensors

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“…This work is, nevertheless, valuable for increasing security by preventing attackers from reconstructing the video [25]. Furthermore, Wang et al enhanced the tracking quality and contributed to the reduction of the moving distance using wireless camera sensor networks [26]. There are issues with this solution in terms of sensing range and distance for tracking targets, which affect the security aspects.…”

Section: Biometric-based Security Techniquesmentioning

confidence: 99%

A Review-Based Taxonomy for Secure Health Care Monitoring: Wireless Smart Cameras

Batchu

Alsadoon

Prasad

et al. 2021

Journal of Applied Security Research

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Health records data security is one of the main challenges in e-health systems. Authentication is one of the essential security services to support the stored data confidentiality, integrity, and availability. This research focuses on the secure storage of patient and medical records in the healthcare sector where data security and unauthorized access is an ongoing issue. A potential solution comes from biometrics, although their use may be time-consuming and can slow down data retrieval. This research aims to overcome these challenges and enhance data access control in the healthcare sector through the addition of biometrics in the form of fingerprints. The proposed model for application in the healthcare sector consists of Collection, Network communication, and Authentication (CNA) using biometrics, which replaces an existing password-based access control method. A sensor then collects data and by using a network (wireless or Zig-bee), a connection is established, after connectivity analytics and data management work which processes and aggregate the data. Subsequently, access is granted to authenticated users of the application. This IoT-based biometric authentication system facilitates effective recognition and ensures confidentiality, integrity, and reliability of patients' records and other sensitive data. The proposed solution provides reliable access to healthcare data and enables secure access through the process of user and device authentication. The proposed model has been developed for access control to data through the authentication of users in healthcare to reduce data manipulation or theft.

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“…Sensor networks have a wide range of applications in IoT, for examples, the structural health monitoring [1] and multimedia big data communications [2]. And the vision sensor network is the hardware foundation of computer vision related tasks, such as target localization, identification, tracking [10] and so on. The optimal camera placement problems have been studied for decades.…”

Section: Related Workmentioning

confidence: 99%

“…Give a threshold value q t ∈ (0, 1). For each particle, if Q i < q t , then update the position and velocity by (10) and (11) respectively.…”

Section: A Resampling Particle Swarm Optimizationmentioning

confidence: 99%

Solving Optimal Camera Placement Problems in IoT Using LH-RPSO

Wang

Zhang

2020

IEEE Access

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With the increasing need for public security and intelligent life and the development of Internet of Things (IoT), the structure and application of vision sensor network are becoming more and more complex. It is no longer a system with simple static monitoring, but a complex system that can be used for intelligent processing, such as target localization, identification, tracking and so on. In order to accomplish various tasks efficiently, it is important to determine the deployment plan of camera network in advance. Many researches discretize the optimal camera placement problem into a binary integer programming (BIP) problem, which is NP-hard, and put forward some approximate solutions including greedy heuristics, semidefinite programming, simulated annealing, etc. In practice, however, camera parameters include both continuous values (location and orientation) and discrete values (camera type). To get a much more accurate result, we do not discretize the continuous camera parameters any more, on the contrary, we handle the continuous values in continuous domain directly. Meanwhile, a Latin Hypercube based Resampling Particle Swarm Optimization (LH-RPSO) algorithm is proposed to effectively solve the problem. To validate the proposed algorithm, we compared it with standard Particle Swarm Optimization (PSO) and Resampling Particle Swarm Optimization (RPSO). Simulation results for an outdoor planar regions illustrated the efficiency of the proposed algorithm.

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Energy-efficient relay tracking with multiple mobile camera sensors

Cited by 16 publications

References 25 publications

Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach

Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach

A Review-Based Taxonomy for Secure Health Care Monitoring: Wireless Smart Cameras

Solving Optimal Camera Placement Problems in IoT Using LH-RPSO

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