Daytime Preceding Vehicle Brake Light Detection Using Monocular Vision

Hua-Tsung Chen, Yi Chien Wu, Chun Chieh Hsu

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Advanced vehicle safety is a recently emerging issue appealed from the explosive population of car owners. Increasing driver assistance systems have been developed for warning drivers of potential hazards by analyzing the surroundings with sensors and/or cameras. Issuing vehicle deceleration and potential collision, brake lights are particularly important warning signals, allowing of no neglect. In this paper, we propose a vision-based daytime brake light detection system using a driving video recorder, which tends to be widespread used. At daytime, visual features, motions, and appearances of vehicles are highly visible. However, brake lights, on the contrary, are hard to notice due to low contrast between the brakes lights and environments. Without the significant characteristic of light scattering as at night, the proposed system extracts preceding vehicles with taillight symmetry verification, and then integrates both luminance and radial symmetry features to detect brake lights. A detection refinement process using temporal information is also employed for miss recovery. Experiments are conducted on a test data set collected by front-mounted driving video recorders, and the results verify that the proposed system can effectively detect brake lights at daytime, showing its good feasibility in real-world environments.
Original languageEnglish
Pages (from-to)120-131
Number of pages12
JournalIEEE Sensors Journal
StatePublished - Sep 2016


  • Brake signal detection; vehicle detection; signal processing; collision avoidance; driver assistance

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