An algorithm using YOLOv4 and DeepSORT for tracking vehicle speed on highway

Currently, expressways are increasingly developed and expanded. Several highways of Vietnam allow vehicles to travel up to 120 kilometers per hour helping to transport goods quickly and bring a lot of socio-economic benefits. Vehicle monitoring plays an important role in reducing traffic accidents helping to handle violations.The paper proposes a model to identify and monitor car speed on highways. The proposal method uses YOLOv4 combining with DeepSORT for vehicle identification and tracking. We then calculate the speed of car based on video recording and sending back from highway. The execution context is highway where vehicles move very fast. The results show that system meets set requirements with over 90% accuracy and execution times for up to 70 frames per second that is suitable for real systems

Evaluation of loading capacity of corroded reinforced concrete beams using experiment and finite element method

The purpose of this paper is to evaluate the performance of corroded reinforced concrete (RC) beams using experiments and a proposed finite element (FE) model, which is able to consider the reduction of the reinforcement diameter and adhesion force. The developed FE model comprised of three main components including concrete elements, reinforcing bar elements, and adhesion elements, in which the plane cross-section hypothesis was adopted. Thus, the necessary number of elements in the model of corroded RC beam was greatly reduced, while the accuracy of the model was still ensured. An experimental test was employed to verify the developed FE model. The results show that the proposed FE model in this study is capable of modeling RC beams under corrosion effects. Additionally, the rebar diameter and adhesion force have a significant influence on the load-carrying capacity of corroded RC beams. Moreover, a series of experimental tests of corrosive RC beams including 1-month, 2-month, and 3-month corrosion levels was conducted for various exposed times to investigate the influences of the corrosion time on the strength of RC beams. It reveals that the effect of the corrosion time on the strength of RC beams show to be pronounced

An updated list of monogenoidea from marine fishes of Vietnam

In this paper, we updated the list of monogenean species from marine fishes of Vietnam. Taxonomic position of monogenean species were arranged according to the current classification system. A total of 220 monogenean species from 152 marine fish species were listed. Distribution, hosts and references of each species were given. In addition, amendations of taxonomic status of taxa were also updated. 

A deep local and global scene-graph matching for image-text retrieval

Conventional approaches to image-text retrieval mainly focus on indexing visual objects appearing in pictures but ignore the interactions between these objects. Such objects' occurrences and interactions are equivalently useful and important in this field as they are usually mentioned in the text. Scene graph presentation is a suitable method for the image-text matching challenge and obtained good results due to its ability to capture the inter-relationship information. Both images and text are represented in scene graph levels and formulate the retrieval challenge as a scene graph matching challenge. In this paper, we introduce the Local and Global Scene Graph Matching (LGSGM) model that enhances the state-of-the-art method by integrating an extra graph convolution network to capture the general information of a graph. Specifically, for a pair of scene graphs of an image and its caption, two separate models are used to learn the features of each graph’s nodes and edges. Then a Siamese-structure graph convolution model is employed to embed graphs into vector forms. We finally combine the graph-level and the vector-level to calculate the similarity of this image text pair. The empirical experiments show that our enhancement with the combination of levels can improve the performance of the baseline method by increasing the recall by more than 10% on the Flickr30k dataset. Our implementation code can be found at https://github.com/m2man/LGSGM

Graph-based indexing and retrieval of lifelog data

Understanding the relationship between objects in an image is an important challenge because it can help to describe actions in the image. In this paper, a graphical data structure, named “Scene Graph”, is utilized to represent an encoded informative visual relationship graph for an image, which we suggest has a wide range of potential applications. This scene graph is applied and tested in the popular domain of lifelogs, and specifically in the challenge of known-item retrieval from lifelogs. In this work, every lifelog image is represented by a scene graph, and at retrieval time, this scene graph is compared with the semantic graph, parsed from a textual query. The result is combined with location or date information to determine the matching items. The experiment shows that this technique can outperform a conventional method

An experiment in Interactive Retrieval for the lifelog moment retrieval task at imageCLEFlifelog2020.

The development of technology has led to an increase in mobile devices’ use to keep track of individual daily activities, as known as Lifelogging. Lifelogging has raised many research challenges, one of which is how to retrieve a specific moment in response to a user’s information need. This paper presents an efficient interactive search engine for large multimodal lifelog data which is evaluated in the ImageCLEFlifelog2020 Lifelog Moment Retrieval task (LMRT). The system is the modified version of the Myscéal demonstrator used in the Lifelog Search Challenge 2020, with the addition of visual similarity and a new method of visualising results. In interactive experimentation, our system achieved an F1@ 10 score of 0.48 in the official submission but can be significantly improved by implementing a number of post-processing steps

Dialogue-to-Video Retrieval

Recent years have witnessed an increasing amount of dialogue/conversation on the web especially on social media. That inspires the development of dialogue-based retrieval, in which retrieving videos based on dialogue is of increasing interest for recommendation systems. Different from other video retrieval tasks, dialogue-to-video retrieval uses structured queries in the form of user-generated dialogue as the search descriptor. We present a novel dialogue-to-video retrieval system, incorporating structured conversational information. Experiments conducted on the AVSD dataset show that our proposed approach using plain-text queries improves over the previous counterpart model by 15.8% on R@1. Furthermore, our approach using dialogue as a query, improves retrieval performance by 4.2%, 6.2%, 8.6% on R@1, R@5 and R@10 and outperforms the state-of-the-art model by 0.7%, 3.6% and 6.0% on R@1, R@5 and R@10 respectively

DCU Team at the NTCIR-16 RCIR Task

Reading is one of the most common everyday activities. People read through most of their daily context such as during study or for entertainment in their spare time. Despite playing a critical role in our lives, there has been limited research on how people read and how it affects their level of understanding. The NTCIR-16 RCIR challenge is the first collaborative evaluation that aims to automatically measure the reading comprehension of a reader and integrate it as part of the information retrieval process. In this paper, we present our approach for the NTCIR-16 RCIR challenge, in which task participants are required to predict reading comprehension using eye movement signals of the readers. We utilised several conventional machine learning techniques to estimate the level of comprehension and combined it with a language model to perform text retrieval. Our extensive experiments, covering both subject-dependent and subject-independent scenarios, showed that our approach with fine-tuning obtained a Spearman’s coefficient of 0.5993 for the comprehension-evaluation task and nDCG at 0.7296 for the comprehension-based retrieval task