Research On Dynamic Crowd Scene Analysis Under Complicated Weather Condition

The analysis of dynamic crowd scene based on computer vision can be appliedbroadly to several of fields including intelligent surveillance, crowd management,public space design, intelligent transportation, virtual environment, etc. Thefrequent occurrences of large-scale mass incidents come to challenges tointelligent surveillance, in recent years, based on crowd motion analysis. Priormodels concerning crowd abnormity detection does not satisfy the public safesituation, more researches, such as tracking the population status andevolutionary process, analysis of variation of crowd motion, predicting futuretrends and so on, are required. Modeling crowd motion scene facing two majorchallenges, one is the negative impact on analysis result caused by visiondegradation of complicated weather conditions, and the other is the lack ofin-depth investigation for the law of crowd motion based on computer vision.In the thesis, the author discusses how the bad weathers interfere featuresextraction of crowd scene in videos and investigates the law of crowd motion inways of local and holistic aspects; and proposes a classification method ofweather types and a video (captured in dynamic weather environment) restorationmeans. Furthermore, performs a comprehensive analysis for crowd motion statesusing enhanced video dataset. Finally, the problem of video degradation byrain/snow is solved and a crowd scene model is built by using a crowd flowequation and graphic analysis. The proposed model can be used to detect andpredict feature crowd status.Aiming at building a complete crowd motion model in complicated weatherconditions, the main contributions of the thesis is reflected in the following fouraspects:1. A classification method of outdoor videos in complex weather conditionsbased on short time correlation and mean of video is proposed. The studyformulates an autocorrelation function in the time domain to extract pixelintensity feature, and classifying videos employing a linear classifier with aclassification and regression-tree (CART). As a result, the input video isclassified to three types: static, dynamic and non-stationary weather according to different weather conditions. It is worth noting that the dynamic weather video isused as input data in the following study.2. A video denoising and rain/snow removal method is proposed by a videowindow series on-line PCA projection algorithm on video window series.Outdoor video often suffer interferences from bad weathers (rain, snow, fog,illumination changing, high and low temperature, etc.), the degraded videosreduce the accuracy of the crowd scene analysis. Therefore, it is necessary todevelop algorithm about inhibition of weather disturbances as a part of wholecrowd model. Using the proposed model, the moving objects with differentmotion properties can be separated and those non-interesting regions are replacedby background. Consequently, the model outputs enhanced video for subsequentapplication of crowd motion analysis.3. To avoid complex calculation of object segmentation and tracking, a localanalysis method of crowd status without foreground segmentation is proposed.Each local area in a video is treated as an independent linear dynamic system(LDS) in temporal-spatial domain. Local crowd groups are classified employingthe mixture of dynamic texture to get crowd density, and employing a main pathfollowing method to obtain local crowd speed. Next, using the density and speedproperties a crowd flow equation, which holds the relations between crowddensity, speed and flow, is built. The equation overcomes the drawbacks of theuncertainty of crowd motion, describes the local scene state and can be used todetecting abnormal events in key areas.4. A non-parameter clustering graph analysis is put forward in crowdabnormity detecting and motion predicting to fit the lack of generalizationcapability in most existing crowd models. The velocity fields are extracted as abasic dataset in which cluster centers are obtained via Mean Shift (MS) clusteringalgorithm, and all centers and the Euclidean distances between them form anundirected graph that reveals the insight of crowd motion status. Thus, theabnormal events could be detected and future states of the crowd could bepredicted by computing distributions of all graph vertexes in feature space, aswell as testing the deviations between observations and estimates of the edgeweights matrix dynamic system. The proposed method need not reinitialize model,retrain new samples and estimate parameters. The first two items are video preprocessing that completes the weather videoclassification and dynamic weather video restoration task, their output providesmore quality video data for subsequent crowd behavior analysis. The latter twoitems involve crowd scene modeling by local and holistic approaches.The research involves video restoration, local crowd state analysis andpredicting future crowd behavior via a graph method. All parts form an integralmodel of dynamic crowd scene that has the capacity of video denoising, imageenhancement, crowd abnormity detection and state forecasting. It provides afeasible monitoring and forecasting way for crowd behaviors analysis based oncomputer vision techniques.