Research On Some Key Algorithms Of Visual Object Detection And Recognition In ITS

Visual object detection and recognition is an important research direction of theintelligent transportation and computer vision.However, complex background, illuminationchanges, object shielding and so on in practical detection and recognition environment is stillfacing many difficulties, so the robustness and accuracy of handling these proplems should befurther improved.Some key issues of visual object and recognition are researched in this dissertation,which mainly include: accurate segmentation of background, objects and shadows undercomplex scene, accurate classification of the extracted foreground objects, object recognitionunder complex background.Motivated by these isssues, the corresponding solutions areproposed in this thesis.Specific studies as follows:1. A new adaptive fuzzy estimate method for background modeling is proposed.Thismethod models background from the perspective of function estimation, and uses TSK fuzzysystem as the estimated function.In order to train the function estimator, the proposedapproach combines both the particle swarm optimization (PSO) and the recursive leastsquares estimator (RLSE) to optimze the parameters of the premise part and consequent partof the fuzzy system. In order to effectively estimate background, we interpret foregroundsamples as outliers relative to the background ones and so propose an outlier Separatormethod. After the outliers are removed, the obtained results are used to train the fuzzyestimator. This method has the high accuracy and effectiveness in dynamic background,illumination changes, camera vibration and so on.2. A new shadow detection method based on choquet fuzzy integral is proposed. Afterthe foreground area is ectracted, this method chooses color and texture as detection features,and defines their similarity and feature importance degree measure functions. Then, thesemeasures are integrated by using the choquet integral in order to initially find shadow andforeground. Finally, the real the shadow areas is distinguished after the subsequent processingstep.3. A new visual object classification method based on JointBoost I2C distance metric.Because the I2C distance has the expensive computation cost, and it is also easily affected bynoise features. At first, we propose to generate a prototype feature set, which has fewersamples, but more representative.The distance calculation of test image to this set can spendless time. And then, we adopt the idea of JointBoost algorithm, combine multiple I2C distance metric to generate a strong classifier, and also propose a spatial information fusion method.Experimental results show that the proposed method has higher classification performance inforeground object and image classification.4. A new visual object recognition method based on feature vocabulary tree and energyminimization is proposed. This method takes into account the spatial position information oflocal features and the spatial relationships between local features, and combines objectdetection and classification. At first, a large number of features extracted from object imageare filtered. And then, the single feature and concatenated pairwise feaures are respectivelyused to build the vocabulary trees by using hierarchical k-means clustering algorithm, thetree-structure has the advantages of the fast lookup feature localization and calssification.wefinally built an energy function combing the matched class probability results of these twovocabulary trees, and then the visual object location and object type are known by minimizingthe energy function of the sliding window in test image.5. A new visual object recognition method based on optimizing Hough forest cost lossfunction is proposed. At first, we propose an improved offset uncertainty measure, exploitingthe knowledge of the object locations in the training images. And then, we adopt the idea ofBoosting algorithm to learn the adaptive weight distribution over the image patch samples andobject image samples, and respectively optimize the cost loss function of constructing randomtree and Hough forest. At last, we propose an improved class uncertainty measure by usingthe weight distribution of the image patch samples, and also give a learning method ofrandom tree weight based on Hough forest cost loss function.