scholarly journals Hardware Middleware for Person Tracking on Embedded Distributed Smart Cameras

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Ali Akbar Zarezadeh ◽  
Christophe Bobda
Keyword(s):  
Smart Environments ◽  
Smart Camera ◽  
Smart Cameras ◽  
Multiple Camera ◽  
Object Request Broker ◽  
Software And Hardware ◽  
On Chip ◽  
A Site ◽  
Vision Application ◽  
Camera Nodes

Tracking individuals is a prominent application in such domains like surveillance or smart environments. This paper provides a development of a multiple camera setup with jointed view that observes moving persons in a site. It focuses on a geometry-based approach to establish correspondence among different views. The expensive computational parts of the tracker are hardware accelerated via a novel system-on-chip (SoC) design. In conjunction with this vision application, a hardware object request broker (ORB) middleware is presented as the underlying communication system. The hardware ORB provides a hardware/software architecture to achieve real-time intercommunication among multiple smart cameras. Via a probing mechanism, a performance analysis is performed to measure network latencies, that is, time traversing the TCP/IP stack, in both software and hardware ORB approaches on the same smart camera platform. The empirical results show that using the proposed hardware ORB as client and server in separate smart camera nodes will considerably reduce the network latency up to 100 times compared to the software ORB.

FPGA Based Embedded Implementation of Video Summary Generation Scheme in Smart Camera

2011 ◽  
Vol 403-408 ◽  
pp. 516-521 ◽  
Author(s):  
Sanjay Singh ◽  
Srinivasa Murali Dunga ◽  
AS Mandal ◽  
Chandra Shekhar ◽  
Santanu Chaudhury
Keyword(s):  
Detection Algorithm ◽  
Smart Camera ◽  
Camera System ◽  
Smart Cameras ◽  
Embedded Platform ◽  
Video Summary ◽  
Embedded Design ◽  
Multiple Frames ◽  
Computationally Intensive ◽  
Embedded Implementation

In any remote surveillance scenario, smart cameras have to take intelligent decisions to generate summary frames to minimize communication and processing overhead. Video summary generation, in the context of smart camera, is the process of merging the information from multiple frames. A summary generation scheme based on clustering based change detection algorithm has been implemented in our smart camera system for generating frames to deliver requisite information. In this paper we propose an embedded platform based framework for implementing summary generation scheme using HW-SW Co-Design based methodology. The complete system is implemented on Xilinx XUP Virtex-II Pro FPGA board. The overall algorithm is running on PowerPC405 and some of the blocks which are computationally intensive and more frequently called are implemented in hardware using VHDL. The system is designed using Xilinx Embedded Design Kit (EDK).

scholarly journals Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 327 ◽  
Author(s):  
Subhan Ullah ◽  
Lucio Marcenaro ◽  
Bernhard Rinner
Keyword(s):  
Data Transfer ◽  
Single Step ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Smart Camera ◽  
Sensitive Data ◽  
Smart Cameras ◽  
Iot Applications ◽  
Public Verifiability ◽  
Single Receiver

Smart cameras are key sensors in Internet of Things (IoT) applications and often capture highly sensitive information. Therefore, security and privacy protection is a key concern. This paper introduces a lightweight security approach for smart camera IoT applications based on elliptic-curve (EC) signcryption that performs data signing and encryption in a single step. We deploy signcryption to efficiently protect sensitive data onboard the cameras and secure the data transfer from multiple cameras to multiple monitoring devices. Our multi-sender/multi-receiver approach provides integrity, authenticity, and confidentiality of data with decryption fairness for multiple receivers throughout the entire lifetime of the data. It further provides public verifiability and forward secrecy of data. Our certificateless multi-receiver aggregate-signcryption protection has been implemented for a smart camera IoT scenario, and the runtime and communication effort has been compared with single-sender/single-receiver and multi-sender/single-receiver setups.

Animal detections vary among commonly used camera trap models

2017 ◽  
Vol 44 (4) ◽  
pp. 291 ◽  
Author(s):  
Michael M. Driessen ◽  
Peter J. Jarman ◽  
Shannon Troy ◽  
Sophia Callander
Keyword(s):  
Camera Trap ◽  
Camera Traps ◽  
Substantial Proportion ◽  
Multiple Camera ◽  
Animal Activity ◽  
Bait Station ◽  
Animal Images ◽  
A Site ◽  
Expensive Model ◽  
Better Than

Context Understanding how different camera trap models vary in their ability to detect animals is important to help identify which cameras to use to meet the objectives of a study. Aims To compare the efficacy of four camera trap models (representing two commonly used brands of camera, Reconyx and Scoutguard) to detect small- and medium-sized mammals and birds. Methods Four camera models were placed side by side, focused on a bait station, under field conditions, and the numbers of triggers and visits by mammals and birds were compared. Trigger=camera sensor is activated and records an image of an animal. Visit=one or a sequence of triggers containing one or more images of a species, with no interval between animal images greater than 5min. Key results The Scoutguard 530V camera recorded fewer than half of the triggers and visits by all animals that the Reconyx H600, Scoutguard 560K and Keepguard 680V cameras recorded. The latter three cameras recorded similar numbers of visits by mammals, but the Reconyx H600 recorded fewer triggers by medium-sized mammals than the Keepguard 680V. All camera models failed to detect a substantial proportion of the total known triggers and visits by animals, with a greater proportion of visits detected (14–88%) than triggers (5–83%). All camera models recorded images with no animals present (blanks), with Reconyx H600 recording the fewest blank images. Conclusions Camera trap models can vary in their ability to detect triggers and visits by small- and medium-sized mammals and birds. Some cheaper camera models can perform as well as or better than a more expensive model in detecting animals, but factors other than cost may need to be considered. Camera traps failed to detect a substantial proportion of known triggers and visits by animals. Number of visits is a more useful index of animal activity or abundance than number of triggers. Implications Variation in camera performance needs to be taken into consideration when designing or comparing camera surveys if multiple camera models are used, especially if the aim is to compare animal activity or abundance. If maximising the number of animal visits recorded at a site is important, then consideration should be given to using two or more cameras.

scholarly journals Semantic processors of Multicore series for analysis of human emotional condition

2019 ◽  
pp. 57-63 ◽  
Author(s):  
E. S. Yanakova ◽  
A. V. Leontyev ◽  
A. V. Shershakov ◽  
N. F. Rybalchenko
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Emotional State ◽  
Time Signal ◽  
Public Places ◽  
Smart Cameras ◽  
Software And Hardware ◽  
Semantic Image Analysis ◽  
Emotional Condition ◽  
Real Time Signal Processing

This article presents a software and hardware solution to the problem of analyzing the emotional state of people in public places by analyzing the emotional state of people using smart cameras. The article describes technologies for creating smart cameras for semantic image analysis based on the Russian ELcore cores. The stages of semantic image analysis with the purpose of detecting faces and recognizing their emotional state are considered, the most resource‑intensive algorithms on DSP‑cores ELcore, developed by R&D Center ELVEES, are identified and implemented. The general path of image processing on DSPcores of ELcore for the purpose of detecting faces and recognizing the emotional state is no more than 32 ms. It meets the requirements for real‑time signal processing and can be used in cameras for «smart» ecosystems.

scholarly journals Impact of Input Data on Intelligence Partitioning Decisions for IoT Smart Camera Nodes

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1898
Author(s):  
Isaac Sánchez Leal ◽  
Irida Shallari ◽  
Silvia Krug ◽  
Axel Jantsch ◽  
Mattias O’Nils
Keyword(s):  
Image Processing ◽  
Internet Of Things ◽  
Input Data ◽  
Processing System ◽  
Input Image ◽  
Smart Camera ◽  
Image Size ◽  
Depth Analysis ◽  
Camera Nodes ◽  
Node Energy

Image processing systems exploit image information for a purpose determined by the application at hand. The implementation of image processing systems in an Internet of Things (IoT) context is a challenge due to the amount of data in an image processing system, which affects the three main node constraints: memory, latency and energy. One method to address these challenges is the partitioning of tasks between the IoT node and a server. In this work, we present an in-depth analysis of how the input image size and its content within the conventional image processing systems affect the decision on where tasks should be implemented, with respect to node energy and latency. We focus on explaining how the characteristics of the image are transferred through the system until finally influencing partition decisions. Our results show that the image size affects significantly the efficiency of the node offloading configurations. This is mainly due to the dominant cost of communication over processing as the image size increases. Furthermore, we observed that image content has limited effects in the node offloading analysis.

scholarly journals Combining Software and Hardware LCS for Lightweight On-chip Learning

2011 ◽  
pp. 253-265 ◽  
Author(s):  
Andreas Bernauer ◽  
Johannes Zeppenfeld ◽  
Oliver Bringmann ◽  
Andreas Herkersdorf ◽  
Wolfgang Rosenstiel
Keyword(s):  
Software And Hardware ◽  
On Chip

A Flexible and Scalable Cooperative Surveillance Technique Using Integrated Ontology Reasoning

2013 ◽  
Vol 284-287 ◽  
pp. 3246-3250
Author(s):  
Pil Seong Park ◽  
Soo Mi Yang
Keyword(s):  
Surveillance System ◽  
Large Scale ◽  
Multimedia Data ◽  
Smart Camera ◽  
Test Bed ◽  
Standard Specification ◽  
Smart Cameras ◽  
Annotation Process ◽  
Smart Camera Networks ◽  
Over Time

In large-scale smart camera networks, cooperation among devices is required for continuous tracking of targets and higher level reasoning. A large amount of multimedia data with derived metadata is generated and transferred among devices. In this paper, we design a large-scale surveillance system which consists of smart cameras. It complies with the standard specification to ensure interoperability among cameras and flexibility regarding integration of new devices and services. Surveillance data contained in them is integrated and structured according to the ontology, and useful context information can be derived. This paper introduces how to build surveillance knowledge base, import relevant data from other devices, and annotate data on interoperable framework which accommodates to the standard. The annotation process provides an impetus to the improvement of knowledge over time. We define a representative reasoning architecture that provides location-based context induction, and implemented in our test bed site to show superiority in large-scale surveillance.

scholarly journals Smart Cameras in Embedded Systems

2020 ◽  
pp. 1-18
Keyword(s):  
Real Time ◽  
Markov Models ◽  
Vision System ◽  
Body Parts ◽  
Smart Camera ◽  
Real Time System ◽  
Smart Cameras ◽  
Media Processor ◽  
Real Time Analysis ◽  
Novel Algorithms

A smart camera performs real-time analysis to recognize scenic elements. Smart cameras are useful in a variety of scenarios: surveillance, medicine, etc. We have built a real-time system for recognizing gestures. Our smart camera uses novel algorithms to recognize gestures based on low-level analysis of body parts as well as hidden Markov models for the moves that comprise the gestures. These algorithms run on a Tri media processor. Our system cans recognize gestures at the rate of 20 frames /second. The camera can also fuse the results of multiple cameras. The smart camera – a whole vision system contained in one neat housing can be used anywhere, in any industry where image processing can be applied. Companies no longer need a cabinet in which to keep all their computing equipment: the computer is housed within the smart camera. In the pharmaceutical industry and in clean rooms – when not even dust is allowed – this can be a big advantage. A single square meter of space can be comparatively very expensive if there is no need for a component rack or cabinet, simply a smart camera, and then this could save a lot of money.

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