Nowadays, many places use security cameras. Unfortunately, when an incident occurs, these technologies are used to show past events. So it can be considered as a deterrence tool than a detection tool. In this article, we will propose a deep learning approach trying to solve this problematic. This approach uses convolutional models (CNN) to extract relevant characteristics linked to the video images, theses characteristics will form times series to be analyzed by LSTM / GRU models.
Priya Martin, Lucylynn Lizarondo, Saravana Kumar
et al.
Introduction Many healthcare workers have switched from face-to-face clinical supervision to telesupervision since the onset of the COVID-19 pandemic. Given the rise in prevalence of telesupervision and continuing remote working arrangements, telesupervision is no longer only limited to rural areas. As this remains an under-investigated area, this study aimed to explore supervisor and supervisee first hand experiences of effective telesupervision. Methods A case study approach combining in-depth interviews of supervisors and supervisees, and document analysis of supervision documentation was used. De-identified interview data were analysed through a reflective thematic analysis approach. Results Three supervisor-supervisee pairs from occupational therapy and physiotherapy provided data. Data analysis resulted in the development of four themes: Benefits vs limitations and risks, not often a solo endeavour, importance of face-to-face contact, and characteristics of effective telesupervision. Discussion Findings of this study have confirmed that telesupervision is suited to supervisees and supervisors with specific characteristics, who can navigate the risks and limitations of this mode of clinical supervision. Healthcare organisations can ensure availability of evidence-informed training on effective telesupervision practices, as well as investigate the role of blended supervision models to mitigate some risks of telesupervision. Further studies could investigate the effectiveness of utilising additional professional support strategies that complement telesupervision, including in nursing and medicine, and ineffective telesupervision practices.
In this study, a novel multiple-frame based image and texture independent convolutional Neural Network (CNN) noise estimator is introduced. The estimator works.
The use of relative attribute (e.g., beautiful, safe, convenient) -based image embeddings in visual place recognition, as a domain-adaptive compact image descriptor that is orthogonal to the typical approach of absolute attribute (e.g., color, shape, texture) -based image embeddings, is explored in this paper.
Handwritten character recognition is an active research challenge,especially for Indian scripts. This paper deals with handwritten Malayalam, with a complete set of basic characters, vowel and consonant signs and compound characters that may be present in the script. Spatial domain features suitable for recognition are chosen in this work. For classification, k-NN, SVM and ELM are employed
Online shopping has gained popularity recently. This paper addresses one crucial problem of buying dress online, which has not been solved yet. This research tries to implement the idea of clothes swapping with the help of DeepFashion dataset where 6,223 images with eight landmarks each used. Deep Convolutional Neural Network has been built for Landmark detection.
Traditional self-attention mechanisms in convolutional networks tend to use only the output of the previous layer as input to the attention network, such as SENet, CBAM, etc. In this paper, we propose a new attention modification method that tries to get the output of the classification network in advance and use it as a part of the input of the attention network. We used the auxiliary classifier proposed in GoogLeNet to obtain the results in advance and pass them into attention networks. we added this mechanism to SE-ResNet for our experiments and achieved a classification accuracy improvement of at most 1.94% on cifar100.
The paper describes our experience collecting a new dataset for the light source estimation problem in a single image. The analysis of existing color targets is presented along with various technical and scientific aspects essential for data collection. The paper also contains an announcement of an upcoming 2-nd International Illumination Estimation Challenge (IEC 2020).
This paper is a brief report to our submission to the VIPriors Object Detection Challenge. Object Detection has attracted many researchers' attention for its full application, but it is still a challenging task. In this paper, we study analysis the characteristics of the data, and an effective data enhancement method is proposed. We carefully choose the model which is more suitable for training from scratch. We benefit a lot from using softnms and model fusion skillfully.
On an example of solution of the face recognition problem the approach for estimation of the most representative descriptor among a set of feature vectors for the same face is considered in present study. The estimation is based on robust calculation of the mode-median mixture vector for the set as the descriptor by means of Welsch/Leclerc loss function application in case of very sparse filling of the feature space with feature vectors
In this work, we provide geometric interpretations of the normalized epipolar error. Most notably, we show that it is directly related to the following quantities: (1) the shortest distance between the two backprojected rays, (2) the dihedral angle between the two bounding epipolar planes, and (3) the $L_1$-optimal angular reprojection error.
In this paper we present an efficient implementation using triplet loss for face recognition. We conduct the practical experiment to analyze the factors that influence the training of triplet loss. All models are trained on CASIA-Webface dataset and tested on LFW. We analyze the experiment results and give some insights to help others balance the factors when they apply triplet loss to their own problem especially for face recognition task. Code has been released in https://github.com/yule-li/MassFace.
This paper primarily focuses on figuring out the best array of cameras, or visual sensors, so that such a placement enables the maximum utilization of these visual sensors. Maximizing the utilization of these cameras can convert to another problem that is simpler for the formulation, that is, maximizing the total coverage with these cameras. To solve the problem, the coverage problem is first defined subject to the capabilities and limits of cameras. Then, poses of cameras are analyzed for the best arrangement.
Photorealistic style transfer is a technique which transfers colour from one reference domain to another domain by using deep learning and optimization techniques. Here, we present a technique which we use to transfer style and colour from a reference image to a video.
The article describes developed information technology for online recognition of handwritten mathematical expressions that based on proposed approaches to handwritten symbols recognition and structural analysis.
It is a well observed phenomenon that natural images are smooth, in the sense that nearby pixels tend to have similar values. We describe a mathematical model of images that makes no assumptions on the nature of the environment that images depict. It only assumes that images can be taken at different scales (zoom levels). We provide quantitative bounds on the smoothness of a typical image in our model, as a function of the number of available scales. These bounds can serve as a baseline against which to compare the observed smoothness of natural images.
Our Multi-Column Deep Neural Networks achieve best known recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human performance.
Watersheds have been defined both for node and edge weighted graphs. We show that they are identical: for each edge (resp.\ node) weighted graph exists a node (resp. edge) weighted graph with the same minima and catchment basin.