55 research outputs found

    Automatic video censoring system using deep learning

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    Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications

    A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

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    [EN] This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as "objects" to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-Subtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein.Suarez-Paez, J.; Salcedo-Gonzalez, M.; Climente, A.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE.; Pérez Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information. 10(12):1-19. https://doi.org/10.3390/info10120365S1191012Wang, L., Rodriguez, R. M., & Wang, Y.-M. 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Human Action Recognition using 3D Convolutional Neural Networks with 3D Motion Cuboids in Surveillance Videos. Procedia Computer Science, 133, 471-477. doi:10.1016/j.procs.2018.07.059Kamel, A., Sheng, B., Yang, P., Li, P., Shen, R., & Feng, D. D. (2019). Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(9), 1806-1819. doi:10.1109/tsmc.2018.2850149Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2018). Real-Time Action Recognition With Deeply Transferred Motion Vector CNNs. IEEE Transactions on Image Processing, 27(5), 2326-2339. doi:10.1109/tip.2018.2791180Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016). Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1), 142-158. doi:10.1109/tpami.2015.2437384Suarez-Paez, J., Salcedo-Gonzalez, M., Esteve, M., Gómez, J. A., Palau, C., & Pérez-Llopis, I. (2018). Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia. International Journal of Computational Intelligence Systems, 12(1), 123. doi:10.2991/ijcis.2018.25905186Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. doi:10.1109/tpami.2016.2577031Hao, S., Wang, P., & Hu, Y. (2019). Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction. Information, 10(2), 81. doi:10.3390/info10020081Peng, M., Wang, C., Chen, T., & Liu, G. (2016). NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification. 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Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings. Future Generation Computer Systems, 82, 349-357. doi:10.1016/j.future.2017.09.082Jha, S., Dey, A., Kumar, R., & Kumar-Solanki, V. (2019). A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network. International Journal of Interactive Multimedia and Artificial Intelligence, 5(5), 30. doi:10.9781/ijimai.2018.08.004Cho, S., Baek, N., Kim, M., Koo, J., Kim, J., & Park, K. (2018). Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network. Sensors, 18(9), 2995. doi:10.3390/s18092995Zhang, J., Xing, W., Xing, M., & Sun, G. (2018). Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network. Sensors, 18(7), 2327. doi:10.3390/s18072327Bakheet, S., & Al-Hamadi, A. (2016). A Discriminative Framework for Action Recognition Using f-HOL Features. Information, 7(4), 68. doi:10.3390/info7040068(2018). Robust Eye Blink Detection Based on Eye Landmarks and Savitzky–Golay Filtering. Information, 9(4), 93. doi:10.3390/info9040093Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Jetson Embedded Development Kit|NVIDIAhttps://developer.nvidia.com/embedded-computingNVIDIA TensorRT|NVIDIA Developerhttps://developer.nvidia.com/tensorrtNVIDIA DeepStream SDK|NVIDIA Developerhttps://developer.nvidia.com/deepstream-sdkFraga-Lamas, P., Fernández-Caramés, T., Suárez-Albela, M., Castedo, L., & González-López, M. (2016). A Review on Internet of Things for Defense and Public Safety. Sensors, 16(10), 1644. doi:10.3390/s16101644Gomez, C., Shami, A., & Wang, X. (2018). Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks. Sensors, 18(11), 3779. doi:10.3390/s18113779AMD Embedded RadeonTMhttps://www.amd.com/en/products/embedded-graphic

    Crowd behaviour and congestion analysis through deep machine learning

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    This thesis looks to advance understanding in the field of computer vision based crowd analysis through a combination of deep learning techniques, multi-task learning, and domain adaptation. Issues that have limited progress in this field to date include visual occlusion, scale and perspective issues, variation in scene content as well as a lack of labelled training data. Another negative trend that has emerged in this field as well as in computer vision in general is the development of bespoke, single-task techniques that cannot be easily extended or re-used. The core contributions of this work are as follows. First, deep learning methods are developed for several crowd analysis tasks including crowd counting, crowd density level estimation, crowd behaviour recognition and crowd behaviour anomaly detection. The proposed data-driven methods are shown to be superior to techniques which rely on hand-crafted features, overcoming many of the observed challenges and achieving state-of-the-art results. Second, multi-task learning strategies are applied to crowd behaviour and congestion analysis tasks, increasing the overall predictive performance and removing redundant model parameters. Finally, domain adaptation techniques are investigated as a means to extend a given crowd analysis model to perform the same task in new visual domains (e.g. medical, wildlife) and vice-versa, with original domain performance preserved

    Advances in Image Processing, Analysis and Recognition Technology

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    For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

    Detection and Classification of Multiple Person Interaction

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    Institute of Perception, Action and BehaviourThis thesis investigates the classification of the behaviour of multiple persons when viewed from a video camera. Work upon a constrained case of multiple person interaction in the form of team games is investigated. A comparison between attempting to model individual features using a (hierarchical dynamic model) and modelling the team as a whole (using a support vector machine) is given. It is shown that for team games such as handball it is preferable to model the whole team. In such instances correct classification performance of over 80% are attained. A more general case of interaction is then considered. Classification of interacting people in a surveillance situation over several datasets is then investigated. We introduce a new feature set and compare several methods with the previous best published method (Oliver 2000) and demonstrate an improvement in performance. Classification rates of over 95% on real video data sequences are demonstrated. An investigation into how the length of time a sequence is observed is then performed. This results in an improved classifier (of over 2%) which uses a class dependent window size. The question of detecting pre/post and actual fighting situations is then addressed. A hierarchical AdaBoost classifier is used to demonstrate the ability to classify such situations. It is demonstrated that such an approach can classify 91% of fighting situations correctly

    Multimedia Retrieval

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    Vision-based representation and recognition of human activities in image sequences

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    Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013von Samy Sadek Mohamed Bakhee

    On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

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    This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

    Visual motion estimation and tracking of rigid bodies by physical simulation

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    This thesis applies knowledge of the physical dynamics of objects to estimating object motion from vision when estimation from vision alone fails. It differentiates itself from existing physics-based vision by building in robustness to situations where existing visual estimation tends to fail: fast motion, blur, glare, distractors, and partial or full occlusion. A real-time physics simulator is incorporated into a stochastic framework by adding several different models of how noise is injected into the dynamics. Several different algorithms are proposed and experimentally validated on two problems: motion estimation and object tracking. The performance of visual motion estimation from colour histograms of a ball moving in two dimensions is improved considerably when a physics simulator is integrated into a MAP procedure involving non-linear optimisation and RANSAC-like methods. Process noise or initial condition noise in conjunction with a physics-based dynamics results in improved robustness on hard visual problems. A particle filter applied to the task of full 6D visual tracking of the pose an object being pushed by a robot in a table-top environment is improved on difficult visual problems by incorporating a simulator as a dynamics model and injecting noise as forces into the simulator.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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