Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring

Rasti, Pejman; Uiboupin, Tõnis; Escalera, Sérgio; Anbarjafari, Gholamreza

doi:10.1007/978-3-319-41778-3_18

Cited by 144 publications

(70 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem above, however, can be framed as a time-series estimation and prediction one, for which numerous machine learning algorithms can be applied. An emerging trend in machine learning for computer vision and pattern recognition is deep learning (DL) which has been successfully applied in a variety of fields, e.g., multi-class classification [7], collaborative filtering [8], image quality assessment [9], reinforcement learning [10], transfer learning [11], information retrieval [12], depth estimation [13], face recognition [14], and activity recognition [15]. Most related to this work are temporal-based deep learners, e.g., [16,17], which we briefly review next.…”

Section: Introductionmentioning

confidence: 99%

Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines

Mocanu

Ammar

Puig

et al. 2017

Pattern Recognition

View full text Add to dashboard Cite

Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficult problem due to uncertainty in the trajectories and environment, high dimensionality of the specific trajectory states, lack of enough labeled data and so on. In this article, we propose a solution to solve this problem based on a novel deep learning model dubbed disjunctive factored four-way conditional restricted Boltzmann machine (DFFW-CRBM). Our method improves state-of-the-art deep learning techniques for high dimensional time-series modeling by introducing a novel tensor factorization capable of driving forth order Boltzmann machines to considerably lower energy levels, at no computational costs. DFFW-CRBMs are capable of accurately estimating, recognizing, and performing near-future prediction of three-dimensional trajectories from their 2D projections while requiring limited amount of labeled data. We evaluate our method on both simulated and real-world data, showing its effectiveness in predicting and classifying complex ball trajectories and human activities.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines

Mocanu

Ammar

Puig

et al. 2017

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…Inspired by biological processes, CNNs are designed to obtain a large number of connections, which in combination can similarly respond to stimuli, such as the visual cortex [Cichy et al 2016]. Among the applications currently are the recognition of patterns in video and images [Feichtenhofer et al 2016], diagnosis and analysis of medical images [Rajpurkar et al 2017, Esteva et al 2017, object detection in images [Liu et al 2016 and surveillance monitoring systems [Rasti et al 2016].…”

Section: Introductionmentioning

confidence: 99%

Mice Tracking Using The YOLO Algorithm

Menezes¹,

Luiz²,

Henrique-Alves³

et al. 2020

Anais Do Seminário Integrado De Software E Hardware (SEMISH 2020)

View full text Add to dashboard Cite

The computational tool developed in this study is based on convolutional neural networks and the You Only Look Once (YOLO) algorithm for detecting and tracking mice in videos recorded during behavioral neuroscience experiments. We analyzed a set of data composed of 13622 images, made up of behavioral videos of three important researches in this area. The training set used 50% of the images, 25% for validation, and 25% for the tests. The results show that the mean Average Precision (mAP) reached by the developed system was 90.79% and 90.75% for the Full and Tiny versions of YOLO, respectively. Considering the high accuracy of the results, the developed work allows the experimentalists to perform mice tracking in a reliable and non-evasive way.

show abstract

“…Skeleton tracking has become much easier with the appearance of motion capture systems, which automatically generate the human skeleton represented by 3-dimensional (3D) coordinates. Additionally, it brought up an increase of research on body movement, such as unusual event detection and crime prevention [ 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition from Skeletal Movements

Sapiński

Kamińska

Pelikant

et al. 2019

Entropy

Self Cite

View full text Add to dashboard Cite

Automatic emotion recognition has become an important trend in many artificial intelligence (AI) based applications and has been widely explored in recent years. Most research in the area of automated emotion recognition is based on facial expressions or speech signals. Although the influence of the emotional state on body movements is undeniable, this source of expression is still underestimated in automatic analysis. In this paper, we propose a novel method to recognise seven basic emotional states—namely, happy, sad, surprise, fear, anger, disgust and neutral—utilising body movement. We analyse motion capture data under seven basic emotional states recorded by professional actor/actresses using Microsoft Kinect v2 sensor. We propose a new representation of affective movements, based on sequences of body joints. The proposed algorithm creates a sequential model of affective movement based on low level features inferred from the spacial location and the orientation of joints within the tracked skeleton. In the experimental results, different deep neural networks were employed and compared to recognise the emotional state of the acquired motion sequences. The experimental results conducted in this work show the feasibility of automatic emotion recognition from sequences of body gestures, which can serve as an additional source of information in multimodal emotion recognition.

show abstract

Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring

Cited by 144 publications

References 24 publications

Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines

Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines

Mice Tracking Using The YOLO Algorithm

Emotion Recognition from Skeletal Movements

Contact Info

Product

Resources

About