Research On Adaptive Updating Of Background Models Based On Intensity-level Migration Statistics

Background modeling (BGM) is a key technology in intelligent video surveillance,and its performance will determine the realization and robustness of various high-levelintelligent video analyses. Over the past decade, the study of BGM has been a popular,but challenging topic in the fields of video analysis and security monitoring. Therefore,it has both theoretical and engineering significance to carry out studies related to BGM.So far most BGM methods have insufficient practicability, due to the complexityand diversity of real-world scenes. The core problem is that a built background modelcannot rapidly and effectively learn all kinds of random changes in the temporal andspatial dimensions of the scenes. Hence, the study of adaptive background modelupdating is a critical step in BGM’s practical application. The existing popular methodsof adaptive background model updating usually have the following drawbacks: Initiallearning rates of the background models must be set manually, which leads toinsufficient adaptability; The learning rate control schemes usually depend on specificbackground models, which leads to poor generality; The pixel-wise calculation oflearning rates is needed, which leads to low efficiency. To overcome the abovedrawbacks of the traditional methods, a novel method of adaptive background modelupdating is proposed in this thesis. The main work of the thesis is as follows:①Inspired by the model of atomic energy level transition in physics, the thesisproposes that the pixels’ intensity changes in videos can be interpreted as the migrationsof pixel samples between different intensity levels. On this basis, a new paradigm oflow-level video data mining for surveillance videos, called intensity-level migrationstatistics (IMS), is proposed. Compared to three traditional paradigms of low-levelvideo data mining (i.e., pixel-based, regional-based, and subspace-based paradigms),IMS can mine unique statistical information that the traditional paradigms cannot obtainfrom surveillance videos. It is proved that the statistical information mined by IMS canbe effectively applied to control the adaptive background model updating.②To resolve the drawbacks of traditional adaptive background model updatingmethods, an IMS-based global method of adaptive background model updating isproposed. By calculating the statistics of the intensity-level migrations of pixels withinthe global surveillance scene, the method can generate a two-dimensional discreteprobability function called global intensity-level migration probability map (IMPM). On this basis, the global IMPM is utilized as an online learning rate lookup table, which isemployed to rapidly retrieve the suitable adaptive learning rates for background modelupdating. This method has the following advantages:1) It has good generality since thelearning rate generation is independent of background models;2) It has goodadaptability since there is no need to manually set any initial learning rate;3) It hasgood computational efficiency since all pixels’ learning rates can be rapidly retrievedfrom a lookup table. Experimental results show that the proposed method caneffectively enhance background models’ adaptability and robustness.③For certain surveillance videos with complex regional scene dynamics, eorrsmight occur in the above global IMPM. To improve the global adaptive backgroundmodel updating method proposed in②, an IMS-based regional method of adaptivebackground model updating is proposed. The method consists of the following steps:1)Adaptive scene dynamics estimation;2) Scene-dynamics based adaptive scenesegmentation;3) The generation of regional IMPMs by calculating the statistics of theintensity-level migrations of pixels within different scene regions;4) To utilize theregional IMPMs as the learning rate lookup tables for the corresponding regions.Experimental results show that the regional adaptive updating method can effectivelyovercome the defect of the global adaptive background model updating method.④When certain particular incidents (e.g., abandoned objects) occur in surveillancescenes, the regional adaptive background model updating method proposed in③maybe ineffective. Hence, an IMS-based adaptive background model updating method forthe particular incident region (PIR) is proposed. The method comprises two parts:1)IMPM-based nonparametric PIR detection and segmentation;2) Adaptive backgroundmodel updating for the PIR based on the human visual perception for jigsaw puzzles.Finally, the adaptive updating method for PIR is integrated into the regional adaptiveupdating method in③, therefore whose robustness is effectively improved.Through a series of experiments carried on the Changedetection benchmark dataset,it shows that the IMS could have a variety of possible applications and can mine uniqueand valuable statistical information from surveillance videos. Meanwhile, theIMS-based adaptive background model updating method can significantly outperformthe traditional adaptive background model updating methods.