Automatic Number Plate Detection and Recognition (ANPDR) has become of significant interest with the substantial increase in the number of vehicles all over the world. ANPDR is particularly important for automatic toll collection, traffic law enforcement, parking lot access control, and gate entry control, etc. Due to the known efficacy of image processing in this context, a number of ANPDR solutions have been proposed. However, these solutions are either limited in operations or work only under specific conditions and environments. In this paper, we propose a robust and computationally-efficient ANPDR system which uses Deformable Part Models (DPM) for extracting number plate features from training images, Structural Support Vector Machine (SSVM) for training a number plate detector with the extracted DPM features, several image enhancement operations on the extracted number plate, and Optical Character Recognition (OCR) for extracting the numbers from the plate. The results presented in this paper, obtained by long-term experiments performed under different conditions, demonstrate the efficiency of our system. They also show that our proposed system outperforms other ANPDR techniques not only in accuracy, but also in execution time.

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Pedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system would allow. However, it remains difficult to make pedestrian detection systems perform well in highly dynamic environments, often requiring extensive retraining of the algorithms for specific conditions to reach satisfactory accuracy, which, in turn, requires large, annotated datasets captured in these conditions. In this paper, we propose a probabilistic decision-level sensor fusion method based on naive Bayes to improve the efficiency of the system by combining the output of available pedestrian detectors for colour and thermal images without retraining. The results in this paper, obtained through long-term experiments, demonstrate the efficacy of our technique, its ability to work with non-registered images, and its adaptability to cope with situations when one of the sensors fails. The results also show that our proposed technique improves the overall accuracy of the system and could be very useful in several applications.