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Real Time object Recognition And counting system for smart individuals

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Abstract:

Detecting and recognizing Yolo objects in unstructured as well as structured environments is one of the most challenging tasks in computer vision and artificial intelligence research. This paper introduces a new computer vision-based obstacle detection method for mobile technology and its applications. Each individual image pixel is classified as belonging either to an obstacle based on its appearance. The method uses a single lens webcam camera that performs in real-time, and also provides a binary obstacle image at high resolution. In the adaptive mode, the system keeps learning the appearance of the obstacle during operation. The system has been tested successfully in a variety of environments, indoors as well as outdoors, making it suitable for all kinds of hurdles. It also tells us the type of obstacle which has been detected by the system.

 

Existing Method:

  • works poor for multiple moving object detection.
  • Single object detection

 

Drawbacks:

Accuracy less

Poor detection

 

Proposed Method:

  • YOLO object feature comparison and recognition system.
  • Deeplearning

 

Advantages:

High detection

Real time detection

 Block Diagram:

YOLO Object Detection

 

Advantages:

  • Automatized Yolo object recognition and replication system.

 

Applications:

  • High efficient signal transfer systems.

 

 Software Requirement:

  • Python Idle
  • OPENCV

 

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  • Price
    Free
  • Instructor pantech team
  • Duration 15 Hrs
  • Enrolled 0 student
  • Access 3 Months

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