Abstract: The main research content is designing intelligent object detection system which can self learning and improve its detection performance based on online learning method. The system is composed by object detection module and validation module. Online learning classifier was used in the object detection module. Samples, which was used to train the classifier online, are acquired and labeled automatically from validation module. Instead of using another detection algorithm to label the new sample like other online learning framework, we ensure the correct label from particle filter tracking. The likelihood distribution of particle sets are used to verify the object detection results. This can greatly reduce the effort by labeler. Furthermore, in order to reduce the impact of validation error, the Multi-information fusion particle filter method is used to improve the robustness of the online learning object detection system. Experimental results on PETS2006 dataset and bus video dataset are provided to show the adaptive capability and high detection rate.