Large-scale intersections stamped on maps have diverse visual features for detection, while small-scale urban intersections are hard to be identified especially when GPS signals are missing. In this paper, we propose a Hidden Markov Model (HMM) based small-scale intersection detection method utilizing monocular vision. We extract visual cues of road transformations and dynamic vehicles' tracks, and then design an Intersection Scan Model to obtain the potential traversable direction of the current road, which is the primary criterion of the intersection estimation. For better performances, we take the detections of consecutive frames into consideration and finally integrate them into HMM to estimate the probabilities of intersections. Results from KITTI datasets and real-world experiments have shown the functionality of the presented approach.
Intersection scan model and probability inference for vision based small-scale urban intersection detection
2017 IEEE Intelligent Vehicles Symposium (IV) ; 1393-1398
2017-06-01
2098522 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Vision Based Intersection Navigation
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