This article seeks to discover the object categories’ semantic probabilistic model (OSPM) based on statistical test analysis method. We applied this model on road forward environment perception algorithm, including on-road object recognition and detection. First, the image was represented by a set composed of words (local feature regions). Then, found the probability distribution among image, local regions and object semantic category based on the new model. In training, the parameters of the object model are estimated. This is done by using expectation-maximization in a maximum likelihood setting. In recognition, this model is used to classify images by using a Bayesian manner. In detection, the posterios is calculated to detect the typical on-road objects. Experiments release the good performance on object recognition and detection in urban street background.
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