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The massive deployment of IoT devices, broadband and mission critical services are paving the way for 5G communication networks, which will enable massive capacity, zero delay, elasticity and optimal deployment, enhanced security, privacy by design and connectivity to billions of devices with less predictable traffic patterns. This paper targets a very important and demanding application the Preventive...
Use of surveillance cameras as a monitoring tool for home environments, elderly, and children has becoming a common practice. However, people with visual impairments have difficulties in using this kind of device because it relies only on visual information. Towards solving this problem, this work aims to propose a solution that combines deep learning techniques for object recognition in the video...
Palmprint recognition has received in the last 20 years a great deal of the research community's attention. In this paper a new palmprint matching approach based on corner feature point extraction is proposed. A 72-element fixed-length descriptor is used to capture distinctive information of each feature point neighborhood and to build a measure of similarity whilst their coordinates provide a measure...
Security systems at airports, borders, and other public areas require high throughput screening at the same time as a high level of protection against possible threats is achieved. New safe stand-off systems with automatic detection of concealed objects that operate in the submillimeter wave region are being developed to meet the need for fast screening and respected privacy. These new technologies...
Estimating the initial background of a scene is a key prerequisite for several applications in video analytics. In this paper, we present a simple approach that takes into account spatio-temporal motion intensities while estimating the true background. We tested the algorithm on real video sequences from the Scene Background Initialization (SBI) benchmark dataset, and the results show that the algorithm...
The object detection is a challenging problem in computer vision with various potential real-world applications. The objective of this study is to evaluate the deep learning based object detection techniques for detecting drones. In this paper, we have conducted experiments with different Convolutional Neural Network (CNN) based network architectures namely Zeiler and Fergus (ZF), Visual Geometry...
Vision systems become more and more popular to be applied in monitoring tasks such as controlling traffic flows or for security issues. The analysis of target behavior is always based on its observed trajectory, which can be acquired by tracking approaches. Although the fashion of tracking-by-detection is favored by the research community, it still faces challenges like unexpected occlusion caused...
This paper presents an online multiple pedestrian detection and tracking method using unified multi-channel features. The proposed method efficiently utilizes the multi-channel features by sharing them in each module: pedestrian detection, visual tracking, and data association. The multi-channel features are originally generated from the pedestrian detection module, and they represent sufficiently...
Different types of vehicles, such as buses and cars, can be quite different in shapes and details. This makes it more difficult to try to learn a single feature vector that can detect all types of vehicles using a single object class. We proposed an approach to perform vehicle detection with Sub-Classes categories learning using R-CNN in order to improve the performance of vehicle detection. Instead...
The paper presents Park Smart, a solution which aim is to solve the pain of finding a free parking space in public and private areas (e.g. cities, malls, etc.), and hence to optimize parking stalls allocation as well as to increase revenues for the companies which manage them. The proposed solution exploits cutting edge technologies such as IoT, Cloud Computing and Deep Learning.
This work applies the Gaussian Mixture Probability Hypothesis Density (GMPHD) Filter to multi-object tracking in video data. In order to take advantage of additional visual information, Kernelized Correlation Filters (KCF) are evaluated as a possible extension of the GMPHD tracking-by-detection scheme to enhance its performance. The baseline GMPHD filter and its extension are evaluated on the UA-DETRAC...
In a surveillance system, the privacy becomes important since those who are not relevant to an event may be recorded by many surveillance systems. On the other hand, the authenticity of video frames in surveillance systems should be guaranteed if a video is used as evidence. Hence a signature is attached for each frame. However, it is contradictory to provide both privacy and authenticity of a video...
The ICT4Life Open Source framework contains libraries for acquiring and processing data from different sensors, machine learning algorithms for activity recognition, as well as fusion methods of multiple modalities either at an early or at a late stage. The main purpose of the introduced system is to enable an easy customization of patients' monitoring using different types of sensors. Furthermore,...
In this paper, a novel multi-modal method for person identification in indoor environments is presented. This approach relies on matching the skeletons detected by a Kinect v2 device with wearable devices equipped with inertial sensors. Movement features such as yaw and pitch changes are employed to associate a particular Kinect skeleton to a person using the wearable. The entire process of sensor...
Learning spaces at universities are limited in their capacity, while, providing more such places to students, often imposes quite some problems to the responsible institutions. This leads to problems for the students finding adequate space to conduct their studies on the campus. The consequences are, e.g. large queues of students waiting in front of buildings in the morning, especially during the...
The detection of persons from videos is particularly important in many computer vision contexts being an enabling technology for several relevant applications either for security and safety or for business intelligence purposes. The adoption of a depth sensor mounted in a top-view position is often used to achieve high person detection accuracy as it allows to cope effectively with occlusions and...
The growing interest in recent years for gender recognition from face images is mainly attributable to the wide range of possible applications that can be used for commercial and marketing purposes. It is desirable that such algorithms process high resolution video frames acquired by using surveillance cameras in real-time. To the best of our knowledge, however, there are no studies which analyze...
Real-time image processing on low cost embedded systems is still a challenging research area. For this embedded platform, there is a trade-off between accuracy and processing time. We proposed a pedestrian detection method for thermal images that can perform in real-time on a Raspberry Pi embedded system while still keeping the accuracy high. Our detection framework is based on the conventional HOG-based...
We present a method to model and classify trajectory data that come from surveillance videos. Observations of the locations of moving entities are used to estimate their expected velocity in the scene. Such estimation is performed by a Gaussian process regression that enables to approximate probabilistically the expected velocity of entities given some observed evidence in the scene. Subsequently,...
This paper proposes a Deep Learning-based action recognition method from an extremely low-resolution thermal image sequence. The method recognizes daily actions by humans (e.g. walking, sitting down, standing up, etc.) and abnormal actions (e.g. falling down) without privacy concerns. While privacy concerns can be ignored, it is difficult to compute feature points and to obtain a clear edge of the...
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