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Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems-A Review
Luiz G Galvao1, Maysam Abbod1, Tatiana Kalganova1
1Department of Electronic and Electrical Engineering, Brunel University London, Kingston Ln, Uxbridge UB8 3PH, UK.
Sensors (Basel, Switzerland)
|November 13, 2021
Summary
Autonomous vehicles (AVs) use computer vision for navigation. Deep learning methods excel at detecting pedestrians and vehicles, but challenges remain for small or occluded objects and adverse conditions.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Robotics
Background:
- Autonomous vehicles (AVs) promise solutions to traffic issues like accidents and pollution.
- Accurate environmental perception is crucial for AV safety in urban settings.
- Existing reviews often focus on pedestrian or vehicle detection separately.
Purpose of the Study:
- To review computer vision techniques for AV perception systems.
- To investigate methods for detecting pedestrians and vehicles.
- To provide a comprehensive overview of AV perception challenges and solutions.
Main Methods:
- Review of recent literature on computer vision techniques for AVs.
- Focus on object detection methods, particularly for pedestrians and vehicles.
- Analysis of both traditional and Deep Learning (DL) approaches.
Main Results:
- Deep Learning (DL) techniques demonstrate superior performance in pedestrian and vehicle detection compared to traditional methods.
- Current algorithms struggle with detecting small, occluded, or truncated objects.
- Limited research exists on improving detection in adverse lighting and weather conditions.
Conclusions:
- DL is the leading approach for AV object detection.
- Further research is needed to address limitations in detecting challenging objects and in adverse conditions.
- Future work should utilize new, more demanding datasets like PIE and BDD100K.
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