Traffic light recognition (TLR) is an integral component of an intelligent vehicle and advance driver assistance systems (ADAS). At present, most of TLR solutions use vision based system along with prior knowledge of traffic light position (map information and height) provided by supporting sensors like Global Positioning System (GPS) sensor, to obtained high accuracy. In this work, we present a method that performs a real time TLR using only vision sensor and achieve good results. Our TLR process is divided into three stages, viz., traffic light box (TLB) detection, extraction of the glowing area from traffic light box and classification. Here, traffic light box detection is carried out using state-ofthe-art real-time object detection method, You Only Look Once (YOLO). For extraction, we project traffic light box region of interest (ROI) to custom color space and perform the blob analysis. In order to elimination false positives, we introduce light weight efficient classifier model in custom color space. For traffic light states classification, we use support vector machine (SVM) with RGB histogram of the cropped ROI as a feature. Bosch Small Traffic Lights Dataset has been used for the empirical validation of our method and achieving F1 score of 0.94 as a performance benchmark.
In this paper, we are presenting a robust and real-time, vision-based approach to detect speed breaker in urban environments for autonomous vehicle. Our method is designed to detect the speed breaker using visual inputs obtained from a camera mounted on top of a vehicle. The method performs inverse perspective mapping to generate top view of the road and segment out region of interest based on difference of Gaussian and median filter images. Furthermore, the algorithm performs RANSAC line fitting to identify the possible speed breaker candidate region. This initial guessed region via RANSAC, is validated using support vector machine. Our algorithm can detect different categories of speed breakers on cement, asphalt and interlock roads at various conditions and have achieved a recall of ~0.98.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.