EuroCC at SmAgTech EXPO VIRAL!

Within the two-day SmAgTech EXPO VIRAL event held at the University of Donja Gorica in the period from February 23 to 24, 2023 the results of the EuroCC project were presented with a special focus on the application of HPC and Ai/ML for computer vision in smart chicken farms.

Ms. Bojana Malisic presented the project to the visitors

The event gathered over 300 visitors from Montenegro, Bosnia and Herzegovina, Serbia, Slovenia, the Netherlands, including agricultural small and medium-sized enterprises, representatives of the IT sector, and academy. In addition to the aforementioned, the event was attended by students and high school pupils attending educational programs complementary to agriculture, information technology and food technology.

Mr Stevan Cakic presenting a HPC/AI use case in agriculture

Numerous companies in the field of agriculture, food industry, as well as information technologies and systems that presented their products and services at the fair, had the opportunity to learn about the EuroCC project and the advantages offered by advanced computing in the form of HPC technology.As part of the EXPO, the representatives of NCC Montenegro presented a pilot project-experiment within Horizon 2020 for innovative small and medium-sized enterprises, which is related to the application of HPC technologies in agriculture, specifically in poultry farming, where with the help of HPC technology, diseases among poultry are monitored and predicted by applying machine learning.

NCC Montenegro team at VIRAL EXPO 2023
VIRAL EXPO 2023 video (longer version)

Short course and student workshop on IoT and AI

A short course and student workshop on AI and IoT took place on 16 February, 2023 within a dedicated session at the IEEE IT2023 conference. This training event is organized by UDG and NCC Montenegro as a part of implementation of the project called “Competency Training for IoT and AI – InnovateYourFuture” supported by ANSO – Alliance of International Science Organizations, China. The edge AI devices were provided through NVIDIA Academic grant. The workshop is includes introduction to AI and IoT (AIoT), software tools for AI/ML, edge AI, and IoT, and presentation and practical demonstrations. The target audience is MSc, BSc, and high-school students, but others are welcome, too. The conference program is available at the following link. The presenters at the workshop were Tomo Popovic, Stevan Cakic, Ivan Jovovic, Zoja Scekic, Dejan Babic and Igor Culafic. The event involved around 60 attendees, 30 on-site and 30 online.

Live demo for the audience (Edge AI with NVIDIA Jetson Nano)
Around 30 students in-person and 30 online attended the event

More information about the conference is available at the IT2023 conference website (link). The workshop will include introduction to AI and IoT (AIoT), software tools for AI/ML, edge AI, and IoT, and presentation and practical demonstrations. The target audience is MSc, BSc, and high-school students, but others are welcome, too. The conference program is available at the following link.

I. Culafic
Z. Scekic
I. Jovovic
D. Babic
S. Cakic
T. Popovic

IEEE IT2023: Disease Prediction Using ML Algorithms

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023 on 17th February 2023. The paper is titled “Disease Prediction Using Machine Learning Algorithms” and authored by I. Jovovic, D. Babic, T. Popovic, S. Cakic and I. Katnic.

ABSTRACT – This study aimed to investigate the application of machine learning techniques for disease prediction. Three popular machine learning algorithms, Random Forest, Support Vector Machines and Naive Bayes, were employed and their performance was evaluated. Results showed that the best performing model was based on Random Forest algorithm with the average accuracy of 87%. This model has been additionally tuned in order to achieve even better performance, which resulted with 90% accuracy. This study highlights the potential of AI in disease prediction and provides insights into the importance of algorithm selection and tuning for optimal performance.

Mr. Ivan Jovovic presenting at IEEE 2023 conference
The paper will soon be available through IEEE Xplore

IEEE IT2023: Vision-based Vehicle Speed Estimation Using the YOLO Detector and RNN

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023.  The paper is titled “Vision-based Vehicle Speed Estimation Using the YOLO Detector and RNN” and authored by Andrija Peruničić, Slobodan Djukanović and  Andrej Cvijetić

ABSTRACT : The paper deals with vehicle speed estimation using video data obtained from a single camera. We propose a speed estimation method which uses the YOLO algorithm for vehicle detection and tracking, and a recurrent neural network (RNN) for speed estimation. As input features for speed estimation, we use the position and size of bounding boxes around the vehicles, extracted by the YOLO detector. The proposed method is trained and tested on the recently proposed VS13 dataset. The experimental results show that the box position does not bring any improvement in the speed estimation performance. The proposed RNN-based estimator gives an average error of 4.08 km/h using only the area of bounding box as input feature, which significantly outperforms audio-based approaches on the same dataset.

Link : https://ieeexplore.ieee.org/document/10078639

IEEE IT2023: Deep learning-based vehicle speed estimation using the YOLO detector and 1D-CNN

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023. The paper is titled “Deep learning-based vehicle speed estimation using the YOLO detector and 1D-CNN” and authored by Andrej Cvijetić, Slobodan Djukanović and Andrija Peruničić

ABSTRACT : This paper addresses vehicle speed estimation using visual data obtained from a single video camera. The proposed method accurately predicts the speed of a vehicle, using the YOLO algorithm for vehicle detection and tracking, and a one-dimensional convolutional neural network (1D-CNN) for speed estimation. The YOLO algorithm outputs bounding boxes around detected objects in an image, which is, in our case, the vehicle whose speed is to be predicted. As input to our 1D-CNN speed estimation model, we introduce a novel feature based on the change of area of the bounding box around the vehicle. The feature, referred to as the changing bounding box area (CBBA), is obtained by calculating the area of the bounding box, frame-to-frame, as the vehicle approaches the camera. The shape of the CBBA curve remains closely the same for all vehicles, with differences conditioned by the value of the observed vehicle’s speed. The proposed method is trained and tested on the VS13 dataset. Experiments show that it is able to accurately predict the vehicle’s speed with an average error of 2.76 km/h, with the best performing vehicle having the average error of just 1.31 km/h. The proposed method exhibits the robustness as a key advantage, eliminating the need for prior knowledge of real-world dimensions such as the vehicle size, road width, camera distance and angle in relation to the road etc.

Link : https://ieeexplore.ieee.org/document/10078518

IEEE IT2023: Vehicle Speed Estimation From Audio Signals Using 1D Convolutional Neural Networks

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023.

The paper is titled “Vehicle Speed Estimation From Audio Signals Using 1D Convolutional Neural Networks” and authored by Ivana Čavor and Slobodan Djukanović.

ABSTRACT : This paper presents an approach to acoustic vehicle speed estimation using audio data obtained from single-sensor measurements. One-dimensional convolutional neural network (1D CNN) is used to estimate the vehicle’s speed directly from raw audio signal. The proposed approach does not require manual feature extraction and can be trained directly on unprocessed time-domain signals. The VS13 dataset, which contains 400 audio-video recordings of 13 different vehicles, is used for training and testing of the proposed model. Two training procedures have been evaluated and tested, one based on determining optimal number of training epochs and the other based on recording model state with minimal validation loss. The experimental results show that the average estimation error on VS13 is 9.50 km/h and 8.88 km/h, respectively.

Link : https://ieeexplore.ieee.org/document/10078724

EUROCC2 presented at the IEEE IT 2023 conference

EUROCC2 project was presented at the 27th IEEE International Conference – Information Technology – IT2023. The conference took place 15-18 February in Zabljak Montenegro. NCC Montenegro team presented the project on the first day of the conference within the special session dedicated to presentations of EU projects. EUROCC2 project is a continuation of the success we achieved with the previous EUROCC.

Presenting EUROCC2 at IT2023
Dr Luka Filipovic presenting NCC Montenegro team and activities in EUROCC
Presentation took place during the first day of the conference

The presentation covered the results from EUROCC and introduced the activities for the upcoming period in EUROCC2. Also, this event was a great place for newly extended NCC Montenegro team members to meet and discuss the activities in the upcoming period. Our team now includes representatives from University of Donja Gorica (UDG) and University of Montenegro (UCG). IEEE IT2023 conference program is available at the following link.

Dr Tomo Popovic and Dr Luka Filipovic answering the questions from the audience