The imaging conditions in real scenes are complex. Vehicle detection therefore involves many challenges

among which the occlusion problem is one of the most significant ones. In the object detection literature

a deformable part model applicable to rigid object detection is one of the most practical part-based models. However

this model is limited by multi-object occlusion. This phenomenon results in many false negatives with a low detection score in vehicle detection because of the loss of visual information under real clutter. To address this problem

an occlusion compensation model is proposed in this study. This model analyzes the visible probability of parts according to a single viewpoint or to multiple viewpoints to compensate for the insufficiency of part-based models and thus avoid undetected errors. First

the position and similarity of each part are estimated with an appearance model in candidate regions to determine which part is under occlusion and to obtain the appearance and structure score of the object

which may be lower than the normal ones for multi-object occlusion. Second

the visible probability of a single viewpoint only considers occlusion conditions. By contrast

the visible probability of multiple viewpoints presents occlusion states from other components that are calculated to obtain the compensation score for occlusion

which refines the detection score of the occluded regions. Finally

we composite the appearance

structure

and our compensation score into an integrated model to reduce false negatives. Two parameters are important in this phase

namely

part detection threshold and occlusion compensation weight. The former is applied to determine part occlusion

whereas the latter is aimed toward a refinement with the detection score of an occluded object. A high part detection threshold produces a high occlusion compensation score that in turn leads to a high false alarm rate. The condition in occlusion compensation weight is similar. They are therefore carefully selected to control the false alarm rate and decrease undetected errors. Visible probability modeling based on a single viewpoint is suitable for simple cases

in which the visible probabilities at the same level of height are identical. On the contrary

the visible probability based on multiple viewpoints is true for complex cases

in which the visible probability near the visible part is high. The validation

which is qualitatively and quantitatively evaluated with precision-recall curves

is efficient in three data sets. The two data sets are highlighted in popular PASCAL and MSRC

and the remaining data set comes from a real scene. Experiment results show that our model could preserve the false alarm rate and improve the accuracy of vehicle detection under occlusion compared with the state-of-the-art model.