Automatic license plate recognition (ALPR) has made great progress, yet is still challenged by various factors in the real world, such as blurred or occluded plates, skewed camera angles, bad weather, and so on. Therefore, we propose a method that uses a cascade of object detection algorithms to accurately and speedily recognize plates’ contents. In our method, YOLOv3-Tiny, an end-to-end object detection network, is used to locate license plate areas, and YOLOv3 to recognize license plate characters. According to the type and position of the recognized characters, a logical judgment is made to obtain the license plate number. We applied our method to a truck weighing system and constructed a dataset called SM-ALPR, encapsulating pictures captured by this system. It is demonstrated by experiment and by comparison with two other methods applied to this dataset that our method can locate 99.51% of license plate areas in the images and recognize 99.02% of the characters on the plates while maintaining a higher running speed. Specifically, our method exhibits a better performance on challenging images that contain blurred plates, skewed angles, or accidental occlusion, or have been captured in bad weather or poor light, which implies its potential in more diversified practice scenarios.

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Estimating the number of pedestrians based upon surveillance videos and images has been a critical task while implementing intelligent signal controls at intersections. However, this has been a difficult task considering the pedestrian waiting area is an outdoor scenario with complex and time-varying surrounding environment. In this study, a method for estimating pedestrian counts based on multisource video data has been proposed. First, the partial least squares regression (PLSR) model is developed to estimate the number of pedestrians from single-source video (either visible light video or infrared video). Meanwhile, the temporal feature of the scenario (daytime or nighttime) is identified based on visible light video. According to the recognized time periods, pedestrian count detection results from the visible light and infrared video data can be obtained with preset corresponding confidence levels. The empirical experiments showed that this fusion method based on environment perception holds the benefits of 24-hour monitoring for outdoor scenarios at the pedestrian waiting area and substantially improved accuracy of pedestrian counting.