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PEDESTRIAN DETECTION

Pedestrian detection is a critical technology used in various applications, particularly in autonomous driving and advanced driver-assistance systems (ADAS). It involves using sensors and algorithms to detect and track pedestrians in real-time to ensure safety and prevent accidents.

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KEY COMPONENTS OF PEDESTRIAN DETECTION

SENSORS

  • These include cameras, LIDAR, RADAR, and infrared sensors. Cameras capture visual data, while LIDAR and RADAR provide distance and speed information

INTEGRATION WITH VEHICLE SYSTEMS

  • The detected pedestrian data is integrated with the vehicle’s control systems to enable actions such as braking, steering, or alerting the driver.

IMAGE PROCESSING

  • Algorithms process the captured images to identify pedestrians. This involves several steps:
      • Preprocessing: Enhancing image quality, removing noise, and normalizing data.
      • Feature Extraction: Identifying key features that differentiate pedestrians from other objects (e.g., shape, motion, texture).

TRACKING

  • Once detected, pedestrians are tracked across frames to predict their movement and adjust vehicle behaviour accordingly. Techniques include Kalman filters and optical flow analysis.

CHALLENGES

OCCLUSION

  • Pedestrians partially hidden by other objects can be challenging to detect.

VARRIED ENVIRONMENTS

  • Different lighting conditions, weather, and backgrounds can affect detection accuracy.

REAL=TIME PROCESSING

  • The system must process data quickly to make immediate decisions, requiring efficient algorithms and powerful hardware.

APPLICATIONS

AUTONOMOUS VEHICLES

  • Ensuring the safety of pedestrians by detecting and reacting to their presence.

SMART TRAFFIC SYSTEMS

  • Managing pedestrian traffic and reducing accidents at crosswalks.

SURVEILLANCE SYSTEMS

  • Monitoring pedestrian movement in public areas for security purposes.