Hidden and Unknown Object Detection in Video

Object detection is applied to find such actual objects as faces, bicycles and buildings in images and videos. The algorithms executed in object detection normally use extracted features and learning algorithms to distinguish object category. It is often implemented in such processes as image retrieval, security, surveillance and automated vehicle parking system.Objects can be detected through a range of models, including Feature-based object detection, Viola-Jones object detection, SVM classification with histograms of oriented gradients (HOG) features, Image segmentation and blob analysis.For detection of hidden objects in the video the Object-class detection method is used, in which case the object or objects are defined in the video in advance [1][2].The proposed method is based on bitwise XOR comparison [3]. The method (system) detects moving as well as static hidden objects.The developed method detects objects with great accuracy it detects also those hidden objects which have great color resemblance to the background images, which are undetectable for a human eye. There is no need to define or describe the searched object before the detection. Thus, the algorithm does not limit the search of the object depending on its type. The algorithm is developed to detect objects of any type and size. It is calculated so to work in case of weather change as well as at any time during a day irrespective of the brightness of the sun (which leads to the increase or the decrease of the intensity of the brightness of an image) in this way the method works dynamically. A system has been developed to execute the method. Object detection is applied to find such actual objects as faces, bicycles and buildings in images and videos. The algorithms executed in object detection normally use extracted features and learning algorithms to distinguish object category. It is often implemented in such processes as image retrieval, security, surveillance and automated vehicle parking system.Objects can be detected through a range of models, including Feature-based object detection, Viola-Jones object detection, SVM classification with histograms of oriented gradients (HOG) features, Image segmentation and blob analysis.For detection of hidden objects in the video the Object-class detection method is used, in which case the object or objects are defined in the video in advance [1][2].The proposed method is based on bitwise XOR comparison [3]. The method (system) detects moving as well as static hidden objects.The developed method detects objects with great accuracy it detects also those hidden objects which have great color resemblance to the background images, which are undetectable for a human eye. There is no need to define or describe the searched object before the detection. Thus, the algorithm does not limit the search of the object depending on its type. The algorithm is developed to detect objects of any type and size. It is calculated so to work in case of weather change as well as at any time during a day irrespective of the brightness of the sun (which leads to the increase or the decrease of the intensity of the brightness of an image) in this way the method works dynamically. A system has been developed to execute the method.nbs

Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images

We address the problem of fine-grained action localization from temporally untrimmed web videos. We assume that only weak video-level annotations are available for training. The goal is to use these weak labels to identify temporal segments corresponding to the actions, and learn models that generalize to unconstrained web videos. We find that web images queried by action names serve as well-localized highlights for many actions, but are noisily labeled. To solve this problem, we propose a simple yet effective method that takes weak video labels and noisy image labels as input, and generates localized action frames as output. This is achieved by cross-domain transfer between video frames and web images, using pre-trained deep convolutional neural networks. We then use the localized action frames to train action recognition models with long short-term memory networks. We collect a fine-grained sports action data set FGA-240 of more than 130,000 YouTube videos. It has 240 fine-grained actions under 85 sports activities. Convincing results are shown on the FGA-240 data set, as well as the THUMOS 2014 localization data set with untrimmed training videos.Comment: Camera ready version for ACM Multimedia 201

Evaluating Two-Stream CNN for Video Classification

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g., "skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.Comment: ACM ICMR'1

Comparative analysis of students' collective consciousness in the Russia-EU and Russia-China border regions: mathematical modelling

Given the unique diversity of Russian regions, regional studies are becoming particularly important for ensuring the stability and development of Russia. There is an extensive body of literature on the economic and social characteristics of Russian regions, their types and ranking whereas the study of collective consciousness requires further attention. It is the collective consciousness that shapes human activity, the results of which largely determine the development of countries and their regions. The authors study the spiritual sphere of regions, the inner world of people, who are human capital. This study is particularly important in relation to Russian youth, who have become one of the most active social groups. The public demand for the analysis of collective consciousness has been constantly growing. The authors argue that there are regional differences in collective consciousness, which are manifested most prominently in the comparison of eastern and western regions. The growing intensity of interaction between Europe and Asia makes the comparison of the western and eastern border regions of Russia particularly important from the geopolitical point of view. The authors employ the principles of an emerging scientific direction, border regional studies, for a comparative analysis of the collective consciousness of students from two border regions located on the Russia-European Union and Russia-China borders. The authors present the results of the survey they conducted in the Immanuel Kant Baltic Federal University (Kaliningrad) and Amur State University (Blagoveshchensk). They examine the sociological phenomenon of ‘regional consciousness’ and substantiate the criteria for selecting the objects of research. It is the first time in sociology that logistic regression models reflecting the main characteristics of regional consciousness have been built. The article aims to confirm the multiplicity of types of regional consciousness and to demonstrate that in the socially homogeneous group, Russian graduate students, there are still regional differences even in the generally similar assessments of the ongoing social processes

Receptive Field Block Net for Accurate and Fast Object Detection

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201

ANALYSIS OF THE STATE OF CREDIT CONSUMER COOPERATIVES IN THE RUSSIAN FEDERATION

The relevance of the development of the credit consumer cooperatives as a promising segment of the credit market has been substantiated. The state of credit consumer cooperatives has been analyzed in the article. The theoretical foundations of the credit consumer cooperatives have been revealed. The main indicators of the credit consumer cooperatives activity have been adduced and analyzed. Directions of activity of the Central Bank in relation to the credit consumer cooperatives have been reflected. The main problems constraining development of credit consumer cooperatives have been identified. Special attention to the insecurity of property interests of shareholders has been paid. Recommendations for improvement of credit consumer cooperatives activities have been presented. The main conclusion is, that the credit consumer cooperatives have a huge financial and credit potential, but in our country they are not yet sufficiently developed. The mechanisms, adduced in the article, could ensure a steady growth of the credit consumer cooperatives’ activities

Student motivation for professional self-improvement

Problems of arousing student motivation in vocational education are discussed. The authors suggest dealing with these problems by specifying the learner’s vocational self-determination and stimulating their professional growth. The notion of “professional self-improvement of students in the system of secondary vocational education” is introduced. Relations between students’ and teachers’ motivation problems are revealed.Раскрываются проблемы формирования мотивации студентов к профессиональному обучению. Предлагается методика разрешения этих проблем средствами уточнения профессионального самоопределения и профессионального самосовершенствования. Вводится определение понятия «профессиональное саморазвитие студентов профессиональных образовательных организаций среднего профессионального образования». Обозначаются связи между проблемами мотивации у студентов и преподавателей

Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201

Towards Bottom-Up Analysis of Social Food

in ACM Digital Health Conference 201