Key Techniques Of Object Recognition And Detection In Optoelectronic Imaging

Object recognition and detection are the fundamental tasks in the field of computer vision and multimedia applications.The task of object recognition is to determine whether a certain type of object is existed in a given image,and the object detection not only needs to determine whether the image contains the object but also needs to determine its location.In the mission of optoelectronic imaging observation,object recognition and detection play a particularly important role;fast and accurate object recognition and detection can not only provide effective support for the object tracking tasks,but also provide powerful basis for the system's decision-making.Although this field has achieved great improvement,it still faces many challenges in practical applications.For example,it is difficult to obtain the ideal recognition and detection precision under the influence of blur,deformation,partial occlusion,illumination change,clutter background,etc.In order to improve the recognition and detection performance,the common approach is to extract high dimensional features and design complex classification models,which will reduce the recognition and detection speed.With the growth of the image data and the improvement of the demand of system's intelligence,developing the high-precision,real-time and well-adapted approaches have become a hot topic.This dissertation focuses on the practical problems of object recognition and detection in different situations and combines with the methods of image processing,computer vision,machine learning and deep learning for further research.The main contents include:studying the efficiency of object recognition from the aspect of classification model optimization;studying the object detection problem of partially occluded objects,multi-scale objects,multi-orientation objects and small objects from the aspect of feature extraction and representation,efficient feature vector coding and polling,accurate positioning,etc.The main contributions are summarized as follow:(1)A model optimization algorithm is proposed for Kernel SVM,a commonly used classification model in object recognition framework.Although the Kernel SVM has good generalization capability,as the number of support vectors increases,the decision-making cost increases as well.Therefore,we propose a support vector reduction method which reconstructs a simplified subset of original support vectors through the way of cyclic iterations.The experimental results show that the simplified SVM can achieve the generalization capability close to the original Kernel SVM with the reduction of decision-making cost.(2)A method which follows the idea of Hough's voting is proposed to deal with the object detection problem of partial occlusion.The considerations of the problem are from two aspects:robust feature extraction and the acceleration of object detection algorithm.From the aspect of feature extraction,we propose a local feature based on spatial information,and we employ this local feature to build a“feature dictionary”which contains rich appearance information of the object.The local feature is seen as a binary structure<p_f,l_f>,where p_f is the appearance pattern of the object,and l_f is the corresponding location of p_f.This local feature is efficient to estimate the centroid of object.Moreover,in order to reduce the information redundancy of the feature vectors,a fast compression approach based on CS(Compressive Sensing)theory is proposed,which just employs a large random matrix to achieve the fast feature compression.Finally,the local compressed features are combined with AdaBoost to build the classification model.From the experimental results on several datasets,the proposed local compressed features show higher detection performance on the partial occluded object.In addition,the proposed fast compression algorithm also shows high performance in compression speed and precision.(3)A method is proposed to deal with the detection problem of object with multi-orientation and multi-scale.To handle this problem,the traditional detection frameworks usually search from a multi-scale image space by a sliding window way,although they show the favorable detection precision,but slowly.We propose a fast detection framework based on the region proposal way;the detection process of the proposed framework includes two stages:the first stage is coarse detection,which employs fast region-proposal way to select about 700 candidate regions from testing image.Compared with the sliding window fashion,region-proposal way reduces the search space greatly.The second stage is fine detection,which recognizes the object from each candidate region.A fast feature vector coding and pooling approach which combines random forest with partitioned BoW(Bag of Words)model is proposed to build the feature representation of the candidate regions.The advantage of the proposed detection framework is accelerating the detection speed in both stages:reducing the search space and speeding up the feature vector coding and pooling.The experimental results show that the proposed method has favorable performance in detection speed and detection precision.(4)A method based on cascaded CNNs(Convolutional Neural Networks)is proposed to deal with the problem of small object(eg.the object in aerial images)detection,such as low detection precision and inaccurate positioning.Due to the small size and the lack of appearance information,it is difficult to achieve accurate positioning by the classical CNN models.Moreover,using the traditional hand-craft feature is hard to describe the small objects.In this dissertation,we first design an object positioning network based on CNN model,which comprises the feature maps of multiple layers and scales.Traversing the hierarchy feature maps can yield better positioning accuracy for small objects.Secondly,another CNN model is trained for feature extraction and object recognition.Finally,we concatenate the two CNNs and build a detection framework.From the experimental results on two public datasets,the proposed detection framework shows its advantages in small object detection task.