Moving Object Detection Oriented Weather Scene Modeling Approach

The complex and uncertain weather conditions afect every aspects of our human’sdaily life. The visual analysis on the complex manifestations of weather conditions chal-lenge many relevant methodologies and applications in computer vision. On the one hand,the manifestations of weather conditions will lead to a poor visual efect, low data qualityand reduced importance of application. On the other hand, an efective visualization ofweather conditions extracted from outdoor scenes will provide a first-step support on thevirtual reality and vision-aided weather forecast.Narrowing down to outdoor video analysis, object detection, which is regarded as apre-processing module of many practical applications, will inevitably obtain a poor detec-tion resulting from the disturbance of weather conditions. To this end, a robust weatherscene modelling method under severe weather conditions is proposed in this thesis basedon an established general weather modelling framework. More details are provided below.(1) A dynamic region segmentation approach is proposed based on multiple instancelearning (MIL). The dynamic region refers to the set of locations afected by temporalpixel changes in video. As a pre-processing module of complex weather classification, asimple but efective region segmentation approach makes a contribution to the selection ofkey locations representing the weather condition for the classification of diferent weatherconditions. In this research, the dynamic region segmentation is converted to the problemof the MIL. Through the phases of bag description, instance definition, instance sorting,distance measure and MI-based K-means clustering, we accomplish the dynamic regionsegmentation on videos.(2) A quantitative classification method on the visual efects of diferent weatherconditions is proposed. Due to the complex manifestations of weather conditions, threehypotheses are made. Moreover, a two-stage classification scheme is provided. Then,features derived from the spatio-temporal and chromatic space are extracted. Using theserepresentative features, we develop a quantitative classifier based on an experiential deci-sion binary tree associated with C-SVM.(3) A varying temporal window approach is proposed to remove dynamic weatherefects from a video. Particularly, we focus more on the removal of rain and snow ratherthan the detection method in this research. Dynamic weather conditions are detected by integrating an of-line K-means clustering with an on-line parameter maintenance ofGaussian distribution. Moreover, a variable time window containing adaptive backgroundedges is presented for removal of rain and snow.(4) An autoregressive texture-based background model combining short-term andlong-term analysis is established for accurate foreground detection from videos with vary-ing outdoor illumination. Diferent weather scenes can be modelled in terms of theirdiferent visual properties. Firstly, we discuss autocorrelation-based features for the iden-tification of foreground and outdoor illumination variations in short-term sequences, andpropose an adaptive threshold learning approach insensitive to inner-pixel fast illumina-tion variation based on the obtained histograms of intensity diferences between succes-sive frames. Then, an iterative orthogonal least squares (OLS) algorithm is designed toestimate the parameters for the auto regression (AR) model against gradual illuminationchange for background estimation in long-term sequence. Finally, we devise a texturemeasure to eliminate the regional efect of fast illumination variation.(5) A piecewise memorizing framework is proposed, which is capable of subtractinglong period video background under the restriction of memory capacity. Three major con-tributions can be claimed. Firstly, hypotheses of background subtraction indicating whatto recognize and memorize are proposed, taking the metaphors of psychological selectiveattention theory into consideration. Secondly, a prior perception-concerned recognition ofrapid illumination change is presented based on a segmented stationarity test. Thirdly, amemorizing framework based on GMM is put forward for the storage of long period back-ground. This framework is capable of identifying long period background appearances,as well as circumventing numerous typical problems except for semantic feed-back.The above research work on weather scene modelling methods results in robustforeground detection under complex weather conditions. Based on the proposed generalframework of scene modelling for the complex manifestations of weather conditions, thedisturbance of weather conditions can be overcome, and even removed. Besides, the pre-sented piecewise memorizing framework provides a new idea for background modellingwith long period memory.