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Detecting vehicle motions are a progressively significant part in road surveillance and Traffic organizing systems. This paper presents a new Deep Gaussian based mixture model that predicts accurate in detecting vehicle motions. Although the existing arrangements based on conventional Gaussian mixture model which is limited in insufficient of many distinct points which eliminate covariance and solutions relative to infinite likelihood. In the proposed scheme, the deep learning neural network is used for including the more points with nested mixture models. To overcome the effects of adding more points the modification achieved in architecture development. The validation of proposed scheme is achieved with real-time videos and process with scikit learn based model.

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