scholarly journals Object recognition and tracking for surveillance applications using deep learning techniques

2020 ◽  
Author(s):  
Anastasios Dimou
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Learning Techniques ◽  
Surveillance Applications

Object Recognition Using Deep Learning

2019 ◽  
Vol 16 (9) ◽  
pp. 4044-4052 ◽  
Author(s):  
Rohini Goel ◽  
Avinash Sharma ◽  
Rajiv Kapoor
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Object Recognition ◽  
Deep Neural Network ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Learning Frameworks

The deep learning approaches have drawn much focus of the researchers in the area of object recognition because of their implicit strength of conquering the shortcomings of classical approaches dependent on hand crafted features. In the last few years, the deep learning techniques have been made many developments in object recognition. This paper indicates some recent and efficient deep learning frameworks for object recognition. The up to date study on recently developed a deep neural network based object recognition methods is presented. The various benchmark datasets that are used for performance evaluation are also discussed. The applications of the object recognition approach for specific types of objects (like faces, buildings, plants etc.) are also highlighted. We conclude up with the merits and demerits of existing methods and future scope in this area.

scholarly journals Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 423
Author(s):  
Gabriel Díaz ◽  
Billy Peralta ◽  
Luis Caro ◽  
Orietta Nicolis
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Recognition ◽  
Training Model ◽  
Cross Entropy ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Visual Objects ◽  
Learning Techniques ◽  
Proposed Model

Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as co-training, where multiple complementary views are combined using a small amount of labeled data. A simple way to associate views to visual objects is through the application of a degree of rotation or a type of filter. In this work, we propose a co-training model for visual object recognition using deep neural networks by adding layers of self-supervised neural networks as intermediate inputs to the views, where the views are diversified through the cross-entropy regularization of their outputs. Since the model merges the concepts of co-training and self-supervised learning by considering the differentiation of outputs, we called it Differential Self-Supervised Co-Training (DSSCo-Training). This paper presents some experiments using the DSSCo-Training model to well-known image datasets such as MNIST, CIFAR-100, and SVHN. The results indicate that the proposed model is competitive with the state-of-art models and shows an average relative improvement of 5% in accuracy for several datasets, despite its greater simplicity with respect to more recent approaches.

scholarly journals A Deep Convolutional Neural Network to Detect the Existence of Geospatial Elements in High-Resolution Aerial Imagery

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 17 ◽  
Author(s):  
Calimanut-Ionut Cira ◽  
Ramón Alcarria ◽  
Miguel-Ángel Manso-Callejo ◽  
Francisco Serradilla
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Aerial Imagery ◽  
Learning Techniques

This paper tackles the problem of object recognition in high-resolution aerial imagery and addresses the application of Deep Learning techniques to solve a challenge related to detecting the existence of geospatial elements (road network) in the available cartographic support. This challenge is addressed by building a convolutional neural network (CNN) trained to detect roads in high resolution aerial orthophotos divided in tiles (256 × 256 pixels) using manually labelled data.

scholarly journals A REVIEW REGARDING DEEP LEARNING TECHNOLOGY IN MOBILE ROBOTS

1970 ◽  
Vol 5 (1.) ◽  
Author(s):  
Vlad Ovidiu Mihalca ◽  
Flaviu Birouaș ◽  
Florin Avram ◽  
Arnold Nilgesz
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Mobile Robots ◽  
State Of The Art ◽  
Future Research ◽  
Learning Technology ◽  
Learning Techniques ◽  
Industry Applications

Deep Learning usage is spread across many fields of application. This paper presents details from a selected variety of works published in recent years to illustrate the versatility of the Deep Learning techniques, their potential in current and future research and industry applications as well as their state-of-the-art status in vision tasks, where their efficiency is experimentally proven to near 100% accuracy. The presented applications range from navigation to localization, object recognition and more advanced interactions such as grasping.

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