Abstract
In this paper, we aim to develop a computer vision system to detect robustly the ambulances observed from a static camera. Robust and reliable ambulance detection plays an important role for priority systems. These aim to find the shortest possible paths of ambulances till their destination by managing signaling networks with traffic lights. If the emergency vehicle gets stuck in a traffic jam and its arrival at the incident location is delayed, it can cause the loss of lives and property. The approach is based on two main stages: Feature extraction step and classification step. For the first step, we used many descriptors to extract features, such as HOG (Histogram of Oriented Gradient), LBP (Local Binary Patterns), and Gabor filter. Then, we classified these features using machine learning algorithms like SVM (Support Vector Machines) and kNN (K-nearest neighbor). To evaluate the performance of our constructed models, we calculated many metrics: True Positive, False Positive, False Negative, True Negative, accuracy and runtime. In fact, our achieved results show that the ambulance detection system can be successfully exploited for the overall system.
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Sara, B., Noureddine, A., Hajar, B. (2020). Ambulance Detection System. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Lecture Notes in Networks and Systems, vol 92. Springer, Cham. https://doi.org/10.1007/978-3-030-33103-0_3
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DOI: https://doi.org/10.1007/978-3-030-33103-0_3
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