2,458 research outputs found
Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device
Currently, most designers face a daunting task to
research different design flows and learn the intricacies of
specific software from various manufacturers in
hardware/software co-design. An urgent need of creating a
scalable hardware/software co-design platform has become a key
strategic element for developing hardware/software integrated
systems. In this paper, we propose a new design flow for building
a scalable co-design platform on FPGA-based system-on-chip.
We employ an integrated approach to implement a histogram
oriented gradients (HOG) and a support vector machine (SVM)
classification on a programmable device for pedestrian tracking.
Not only was hardware resource analysis reported, but the
precision and success rates of pedestrian tracking on nine open
access image data sets are also analysed. Finally, our proposed
design flow can be used for any real-time image processingrelated
products on programmable ZYNQ-based embedded
systems, which benefits from a reduced design time and provide a
scalable solution for embedded image processing products
Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision
Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy and/or latency concerns. Accordingly, energy-efficient embedded vision hardware delivering real-time and robust performance is crucial. While deep learning is gaining popularity in several computer vision algorithms, a significant energy consumption difference exists compared to traditional hand-crafted approaches. In this paper, we provide an in-depth analysis of the computation, energy and accuracy trade-offs between learned features such as deep Convolutional Neural Networks (CNN) and hand-crafted features such as Histogram of Oriented Gradients (HOG). This analysis is supported by measurements from two chips that implement these algorithms. Our goal is to understand the source of the energy discrepancy between the two approaches and to provide insight about the potential areas where CNNs can be improved and eventually approach the energy-efficiency of HOG while maintaining its outstanding performance accuracy
HOG, LBP and SVM based Traffic Density Estimation at Intersection
Increased amount of vehicular traffic on roads is a significant issue. High
amount of vehicular traffic creates traffic congestion, unwanted delays,
pollution, money loss, health issues, accidents, emergency vehicle passage and
traffic violations that ends up in the decline in productivity. In peak hours,
the issues become even worse. Traditional traffic management and control
systems fail to tackle this problem. Currently, the traffic lights at
intersections aren't adaptive and have fixed time delays. There's a necessity
of an optimized and sensible control system which would enhance the efficiency
of traffic flow. Smart traffic systems perform estimation of traffic density
and create the traffic lights modification consistent with the quantity of
traffic. We tend to propose an efficient way to estimate the traffic density on
intersection using image processing and machine learning techniques in real
time. The proposed methodology takes pictures of traffic at junction to
estimate the traffic density. We use Histogram of Oriented Gradients (HOG),
Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for
traffic density estimation. The strategy is computationally inexpensive and can
run efficiently on raspberry pi board. Code is released at
https://github.com/DevashishPrasad/Smart-Traffic-Junction.Comment: paper accepted at IEEE PuneCon 201
Classification of Humans into Ayurvedic Prakruti Types using Computer Vision
Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine.
This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda
An Energy-Efficient Hardware Implementation of HOG-Based Object Detection at 1080HD 60 fps with Multi-Scale Support
A real-time and energy-efficient multi-scale object detector hardware implementation is presented in this paper. Detection is done using Histogram of Oriented Gradients (HOG) features and Support Vector Machine (SVM) classification. Multi-scale detection is essential for robust and practical applications to detect objects of different sizes. Parallel detectors with balanced workload are used to increase the throughput, enabling voltage scaling and energy consumption reduction. Image pre-processing is also introduced to further reduce power and area costs of the image scales generation. This design can operate on high definition 1080HD video at 60 fps in real-time with a clock rate of 270 MHz, and consumes 45.3 mW (0.36 nJ/pixel) based on post-layout simulations. The ASIC has an area of 490 kgates and 0.538 Mbit on-chip memory in a 45 nm SOI CMOS process.Texas Instruments IncorporatedUnited States. Defense Advanced Research Projects Agency (Young Faculty Award Grant N66001-14-1-4039
Energy-Efficient HOG-based Object Detection at 1080HD 60 fps with Multi-Scale Support
In this paper, we present a real-time and energy-efficient multi-scale object detector using Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classification. Parallel detectors with balanced workload are used to enable processing of multiple scales and increase the throughput such that voltage scaling can be applied to reduce energy consumption. Image pre-processing is also introduced to further reduce power and area cost of the image scales generation. This design can operate on high definition 1080HD video at 60 fps in real-time with a clock rate of 270 MHz, and consumes 45.3 mW (0.36 nJ/pixel) based on post-layout simulations. The ASIC has an area of 490 kgates and 0.538 Mbit on-chip memory in a 45nm SOI CMOS process
Hardware accelerated real-time Linux video anonymizer
Dissertação de mestrado em Engenharia Eletrónica Industrial e ComputadoresOs Sistemas Embebidos estão presentes atualmente numa variada gama de equipamentos do
quotidiano do ser humano. Desde TV-boxes, televisões, routers até ao indispensável telemóvel.
O Sistema Operativo Linux, com a sua filosofia de distribuição ”one-size-fits-all” tornou-se
uma alternativa viável, fornecendo um vasto suporte de hardware, técnicas de depuração, suporte
dos protocolos de comunicação de rede, entre outros serviços, que se tornaram no conjunto
standard de requisitos na maioria dos sistemas embebidos atuais.
Este sistema operativo torna-se apelativo pela sua filosofia open-source que disponibiliza ao
utilizador um vasto conjunto de bibliotecas de software que possibilitam o desenvolvimento num
determinado domínio com maior celeridade e facilidade de integração de software complexo.
Os algoritmos deMachine Learning são desenvolvidos para a automização de tarefas e estão
presentes nas mais variadas tecnologias, desde o sistema de foco de imagem nosmartphone até
ao sistema de deteção dos limites de faixa de rodagem de um sistema de condução autónoma.
Estes são algoritmos que quando compilados para as plataformas de sistemas embebidos,
resultam num esforço de processamento e de consumo de recursos, como o footprint de memória,
que na maior parte dos casos supera em larga escala o conjunto de recursos disponíveis para a
aplicação do sistema, sendo necessária a implementação de componentes que requerem maior
poder de processamento através de elementos de hardware para garantir que as métricas tem porais sejam satisfeitas.
Esta dissertação propõe-se, por isso, à criação de um sistema de anonimização de vídeo
que adquire, processa e manipula as frames, com o intuito de garantir o anonimato, mesmo na
transmissão.
A sua implementação inclui técnicas de Deteção de Objectos, fazendo uso da combinação
das tecnologias de aceleração por hardware: paralelização e execução em hardware especial izado. É proposta então uma implementação restringida tanto temporalmente como no consumo
de recursos ao nível do hardware e software.Embedded Systems are currently present in a wide range of everyday equipment. From TV-boxes,
televisions and routers to the indispensable smartphone.
Linux Operating System, with its ”one-size-fits-all” distribution philosophy, has become a
viable alternative, providing extensive support for hardware, debugging techniques, network com munication protocols, among other functionalities, which have become the standard set of re quirements in most modern embedded systems.
This operating system is appealing due to its open-source philosophy, which provides the
user with a vast set of software libraries that enable development in a given domain with greater
speed and ease the integration of complex software.
Machine Learning algorithms are developed to execute tasks autonomously, i.e., without
human supervision, and are present in the most varied technologies, from the image focus system
on the smartphone to the detection system of the lane limits of an autonomous driving system.
These are algorithms that, when compiled for embedded systems platforms, require an ef fort to process and consume resources, such as the memory footprint, which in most cases far
outweighs the set of resources available for the application of the system, requiring the imple mentation of components that need greater processing power through elements of hardware to
ensure that the time metrics are satisfied.
This dissertation proposes the creation of a video anonymization system that acquires, pro cesses, and manipulates the frames, in order to guarantee anonymity, even during the transmis sion.
Its implementation includes Object Detection techniques, making use of the combination
of hardware acceleration technologies: parallelization and execution in specialized hardware.
An implementation is then proposed, restricted both in time and in resource consumption at
hardware and software levels
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