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Related Concept Videos

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Color Vision01:24

Color Vision

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Transmission-based Precautions II: Airborne and Protective Environment01:25

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Transmission-based precautions are for patients infected or suspected to be infected (or colonized) with organisms posing a significant risk to others. The transmission precautions include airborne and protective environment precautions.
Airborne precautions:
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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Gene-Environment Interactions01:20

Gene-Environment Interactions

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Related Experiment Video

Updated: Jan 28, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Vision-Based People Counting and Tracking for Urban Environments.

Daniyar Nurseitov1, Kairat Bostanbekov1, Nazgul Toiganbayeva2

  • 1KazMunayGas Engineering LLP, Astana 010000, Kazakhstan.

Journal of Imaging
|January 27, 2026
PubMed
Summary

This study introduces an intelligent passenger traffic monitoring system using computer vision and deep learning. The developed YOLO + Tracking architecture accurately detects and counts people in dense urban environments, improving transport efficiency.

Keywords:
computer visiondepth cameramulti-sensor fusionobject trackingpeople countingpublic transport monitoringreal-time processingsmart mobility

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • Urban population growth necessitates intelligent passenger traffic monitoring.
  • Accurate passenger counting is crucial for transport efficiency, safety, and quality.

Purpose of the Study:

  • To develop an automatic people detection and counting system using computer vision and deep learning.
  • To propose a modified DeepSORT tracking pipeline optimized for dense, occluded, and dynamically lit passenger environments.
  • To create a unified architecture integrating detection, tracking, and event-log generation.

Main Methods:

  • Utilized YOLOv8 for object detection and a modified DeepSORT for tracking.
  • Developed a proprietary dataset with 4047 images and 8918 labeled objects.
  • Integrated detection, tracking, and automatic event-log generation into a single architecture.

Main Results:

  • Achieved 92% detection accuracy and 85% counting accuracy on the proprietary dataset.
  • YOLOv8 demonstrated a superior balance of speed, accuracy, and computational efficiency compared to Mask R-CNN and DETR.
  • The developed YOLO + Tracking system effectively combines recognition, tracking, and counting.

Conclusions:

  • Computer vision offers an efficient and scalable alternative to traditional passenger counting systems.
  • The proposed system automatically generates annotated video streams and event logs.
  • Future work includes dataset expansion, multi-camera integration, and adaptation for embedded devices.