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Object Detection, Recognition, and Tracking Algorithms for ADASs-A Study on Recent Trends.

Vinay Malligere Shivanna1, Jiun-In Guo1,2,3

  • 1Department of Electrical Engineering, Institute of Electronics, National Yang-Ming Chiao Tung University, Hsinchu City 30010, Taiwan.

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Summary

Advanced driver assistance systems (ADASs) use object detection, recognition, and tracking algorithms to enhance vehicle safety. This review covers state-of-the-art ADAS algorithms and discusses future research needs for challenging driving conditions.

Keywords:
advanced driver assistance system (ADAS)deep learningobject detectionobject tracking

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Area of Science:

  • Automotive Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Advanced driver assistance systems (ADASs) are increasingly integrated into vehicles to improve safety and driving experience.
  • ADASs utilize sensors like cameras, radars, and lidars for environmental perception.

Purpose of the Study:

  • To review state-of-the-art object detection, recognition, and tracking algorithms in ADAS.
  • To explore recent trends, datasets, and functionalities of ADAS algorithms.

Main Methods:

  • Comprehensive literature review of prominent ADAS algorithms.
  • Analysis of object detection, recognition, and tracking techniques.
  • Examination of datasets used in ADAS research.

Main Results:

  • Overview of current object detection, recognition, and tracking algorithms for ADAS functionalities.
  • Identification of recent trends and commonly used datasets.
  • Discussion on the role of these algorithms in enhancing vehicle safety and performance.

Conclusions:

  • Object detection, recognition, and tracking are crucial for ADAS.
  • Future research should focus on improving algorithms for challenging environments like low visibility and high traffic density.