3,121 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
A Parallel Histogram-based Particle Filter for Object Tracking on SIMD-based Smart Cameras
We present a parallel implementation of a histogram-based particle filter for object tracking on smart cameras based on SIMD processors. We specifically focus on parallel computation of the particle weights and parallel construction of the feature histograms since these are the major bottlenecks in standard implementations of histogram-based particle filters. The proposed algorithm can be applied with any histogram-based feature sets—we show in detail how the parallel particle filter can employ simple color histograms as well as more complex histograms of oriented gradients (HOG). The algorithm was successfully implemented on an SIMD processor and performs robust object tracking at up to 30 frames per second—a performance difficult to achieve even on a modern desktop computer
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
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
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
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