Abstract
Background modelling techniques use the time, spatial, intensity and image plane information to detect the objects. These features are integrated to extract the maximum information. The utilization of background techniques are mostly dependent on various parameters that can be learning rate or threshold. High dependency on parameters increase the complexity and make it difficult to control in changing weather conditions. Parameters based techniques do not provide the high efficiency in outdoor computer vision applications where illumination conditions are difficult to predict. This paper presents an algorithm that is based on background modelling with less dependency on parameters and robust to illumination changes. Camera jitter causes the major effect in modelling techniques so camera jitter is also addressed. A new way of separation of shadow from object is also implemented. Performance of the algorithm is compared with other state-of-the-art methods.
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Iftikhar, Z., Premaratne, P., Vial, P., Yang, S. (2016). Tempo-Spatial Compactness Based Background Subtraction for Vehicle Detection and Tracking. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_9
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