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Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
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The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
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Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
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An FPGA-Based Ultra-High-Speed Object Detection Algorithm with Multi-Frame Information Fusion.

Xianlei Long1,2, Shenhua Hu1,2, Yiming Hu1,2

  • 1The Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

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|August 29, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-frame fusion model to enhance object detection accuracy. The method reduces deviation in high-speed vision systems by intelligently combining image data, improving detection performance.

Keywords:
field-programmable gate arrayhistogram of oriented gradientmulti-frame information fusion modelobject detectionultra-high-speed vision

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Area of Science:

  • Computer Vision
  • Hardware Acceleration
  • Machine Learning

Background:

  • Object detection algorithms face challenges with complex backgrounds and high-speed processing.
  • Existing high-speed vision platforms using Field-Programmable Gate Arrays (FPGAs) exhibit limitations in detection window strides and data transmission bandwidth.
  • Significant image information is lost due to transmission constraints, impacting detection accuracy.

Purpose of the Study:

  • To propose an ultra-high-speed object detection algorithm overcoming limitations of current hardware implementations.
  • To develop a multi-frame information fusion model to compensate for lost image data and reduce detection deviation.
  • To achieve high-speed object detection at 10,000 frames per second (FPS) with improved accuracy.

Main Methods:

  • An algorithm combining Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) is implemented on an FPGA.
  • A multi-frame information fusion model is introduced, regenerating data and fusing information from continuous frames.
  • Maximum HOG feature values and coordinates are stored and compensated through fusion with adjacent frames' data.

Main Results:

  • The proposed multi-frame fusion model effectively reduces deviation in object detection compared to existing methods.
  • The algorithm achieves high-speed processing at 10,000 FPS on the high-speed-vision platform.
  • Improved accuracy in object detection is demonstrated through experimental evaluations.

Conclusions:

  • The multi-frame information fusion model is a viable solution for enhancing object detection in high-speed, bandwidth-limited scenarios.
  • The proposed HOG-SVM algorithm on FPGA offers a significant improvement in detection accuracy and performance.
  • This approach mitigates information loss and reduces deviation, paving the way for more robust real-time vision systems.