Robust and Computationally Lightweight Autonomous Tracking of Vehicle Taillights and Signal Detection by Embedded Smart Cameras

Akhan Almagambetov, Senem Velipasalar, Mauricio Casares

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)

Abstract

An important aspect of collision avoidance and driver assistance systems, as well as autonomous vehicles, is the tracking of vehicle taillights and the detection of alert signals (turns and brakes). In this paper, we present the design and implementation of a robust and computationally lightweight algorithm for a real-time vision system, capable of detecting and tracking vehicle taillights, recognizing common alert signals using a vehicle-mounted embedded smart camera, and counting the cars passing on both sides of the vehicle. The system is low-power and processes scenes entirely on the microprocessor of an embedded smart camera. In contrast to most existing work that addresses either daytime or nighttime detection, the presented system provides the ability to track vehicle taillights and detect alert signals regardless of lighting conditions. The mobile vision system has been tested in actual traffic scenes and the results obtained demonstrate the performance and the lightweight nature of the algorithm.
Original languageEnglish
Pages (from-to)3732-3741
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume62
Issue number6
DOIs
Publication statusPublished - Jun 1 2015

Keywords

  • Vehicles
  • Image color analysis
  • Kalman filters
  • Histograms
  • Three-dimensional displays
  • Algorithm design and analysis
  • Smart cameras
  • Autonomous vehicles
  • collision avoidance systems
  • embedded cameras
  • image processing
  • lightweight algorithms
  • tracking
  • transportation
  • vehicle signal detection

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