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Production of high quality wheat has a great importance especially in the solution of nutrition problems. It is necessary to make decomposition for specifying the quality. Here, high quality and unclassified wheat recognition are realized. The most distinctive feature between high quality and poor quality wheat is the shape difference. In this study, Bag of Contour Fragments (BCF) was used as a shape...
The use of depth sensors in activity recognition is a technology that emerges in human computer interaction and motion recognition. In this study, an approach to identify single-person activities using deep learning on depth image sequences is presented. First, a 3D volumetric template is generated using skeletal information obtained from a depth video. The generated 3D volume is used for extracting...
Feature fusion methods have been demonstrated to be effective for many computer vision based applications. These methods generally use multiple hand-crafted features. However, in recent days, features extracted through transfer leaning procedures have been proved to be robust than the hand-crafted features in myriad applications, such as object classification and recognition. The transfer learning...
In this paper, we presented a comparison between different approaches of person re-identification in camera network based on the-state-of-the-art. We studied the different descriptors of objects for identifying people and existing classifier at the re-identification step. We seek to develop video surveillance systems online in controlled areas and improve their reliability and their processing time...
In this work, we develop a video surveillance system to detect the disappearance of the objects selected by an operator. Proposed method is optimized to minimize the effects of shadows, partial occlusions and changes in illumination to minimize false alarms. The system is trained by extracting local features from the object to produce an alarm in the case of the disappearance.
Learning from imbalanced data sets is an important problem frequently encountered in the application of classification problems. Instances of this type of problem is usually labeled with the label of class majority and minority class instances will be ignored. In this study, an ensemble based method is proposed for problems of imbalanced data set. The results obtained were compared to alternative...
We propose a systematic frame work for the automatic detection of multiple human actions within the same frame in realistic and diverse video settings. One of the major challenges is the process of recognizing and understanding of human actions from videos with large variations resulting from camera motions, changes in human appearance, pose changes, scale changes and back ground clutter etc. In this...
Due to the influence of both background interference and time-varying position, it is difficult to detect the moving object by computer vision in the complex background environment. This paper novelly builds a topological structure of typical features to detect object. The presented method can effectively detect the object which is scaling, rotating or in affine transformation, because of the using...
Abnormal activity detection plays an important role in many areas such as surveillance, military installations, and sports. Existing abnormal activity detectors mostly rely on motion data obtained over a number of frames to characterize abnormality. However, only motion may not be able to capture all forms of abnormality, in particular, poses that do not amount to motion "outliers". In this...
Pedestrian detection is an important area in computer vision with key applications in intelligent vehicle and surveillance systems. One of the main challenges in pedestrian detection is occlusion. In this paper, we propose a novel pedestrian detection approach capable of handling partial occlusion. Three stage cascaded classifier is used in the proposed approach. Global classifier based on HOG features...
The rapid development of worldwide networks has changed many challenge problems from video level to big video level for vision based surveillance. An important technique for big video processing is to extract the salient information from the video datasea effectively. As a fundamental function for data analysis such as behavior understanding for social security, object tracking usually plays an essential...
This paper proposes a Network Shaped Cascade Classifier(NSCC) based on potential functions for pedestrian detection. Potential function is exploited to capture the nonlinear information in the training set based on the multiple sample centers. A flexible structure in NSCC is used to combine the base classifier and potential function into a nonlinear cascade classifier, and NSCC can well inherit the...
Classifier fusion methods are usually used to combine multiple classification decisions and generate better classification results than any single classifier. In order to improve object classification accuracy, it is a common method to assign weights to classifiers based on their importance in a multiple decision system. In this paper we put forward a method to weight different classifiers in classifier...
This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification,...
This paper presents an omnidirectional vision based solution to detect human beings. We first go through the conventional sliding window approaches for human detection. Then, we describe how the feature extraction step of the conventional approaches should be modified for a theoretically correct and effective use in omnidirectional cameras. In this way we perform human detection directly on the omnidirectional...
We present a plant image recognition system geared towards plants with flowers. The system uses local invariants with Dense SIFT features and Bag of Visual Words representation, while the classification is done using Support Vector Machines. Our approach contains a pre-classification stage where images are categorized into color subgroups, to reduce the complexity of the problem. Using a 161-class...
Recognition of video scenes is a challenging problem due to the unconstrained structure of the video content. Here, we propose a spatial pyramid based method for the recognition of video scenes and explore the effect of parameter optimization to the recognition accuracy. In the experiments different sampling methods, dictionary sizes, kernel methods, and pyramid levels are examined. Support Vector...
Analysing and measuring beauty and attractiveness has become a passion since the beginning of the human existence. Providing solutions to this mystery has been the pursuit of philosophers, artists, and anthropologists for centuries. More recently, the computer science community has attempted to propose computational models for the perception and representation of beauty by cross-fertilizing technological...
Photo events in personal photo collection management are important because people take photos according to the specific events. Event is one of important cues to retrieve photos which are well remembered by people. However, event classification work needs some useful cues to annotate the photo events well. Our main contribution is to annotate photo events automatically using simple temporal cues such...
This paper introduces a part-based two-stage pedestrian detector. The system finds pedestrian candidates with an AdaBoost cascade on Haar-like features. It then verifies each candidate using a part-based HOG-SVM doing first a regression and then a classification based on the estimated function output from the regression. It uses the Histogram of Oriented Gradients (HOG) computed on both the full,...
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