Research Class: Aktivosti u sklopu projekta HRZZ “Automatsko raspoznavanje akcija i aktivnosti u multimedijalnom sadržaju iz domene sporta”

Datum održavanja: srijeda, 16.5.2018. u 11:00 sati, prostorija O-403 (Vijećnica Odjela za informatiku)
Predavači: Matija Burić i Mate Krišto
Naziv predavanja: Aktivosti u sklopu projekta HRZZ “Automatsko raspoznavanje akcija i aktivnosti u multimedijalnom sadržaju iz domene sporta”


Sažetak:
 
Predstavit će se aktivnosti u sklopu projekta “Automatsko raspoznavanje akcija i aktivnosti u multimedijalnom sadržaju iz domene sporta” kojemu je cilj razvoj postupaka za detekciju i automatsko raspoznavanje individualnih akcija u multimedijalnom sadržaju iz odabrane domene sporta, razvoj modela za predstavljanje znanja i tumačenje aktivnosti iz te domene te definiranje metrike za usporedbu izvedbe akcije s referentnom izvedbom te akcije.

Naglasak će biti na predstavljanju istraživanja vezanih za detekciju objekata i metode za detekciju lica.
 
Object Detection in Sports Videos
 
Object detection is commonly used in many computer vision applications. In our case, we need to apply the object detector as a prerequisite for action recognition in handball scenes. Object detection, to be successful for this task, should be as accurate as possible and should be able to deal with a different number of objects of various sizes, partially occluded, with bad illumination and deal with cluttered scenes. The aim is to provide an overview of the current state-of-the-art detection methods that rely on convolutional neural networks (CNNs) and test their performance on custom video sports materials acquired during handball training and matches. The comparison of the detector performance in different conditions will be given and discussed.

An Overview of Thermal Face Recognition Methods
 
The popularity of surveillance and access control systems grows as well as a need for better security systems particularly in bad lighting conditions or at night. The aim of a security system is to collect as many details as possible to enable a better recognition of persons. A comparison of representative thermal face recognition methods will be given, emphasizing their strengths and weaknesses. Then, trends in the development of surveillance and security systems will be outlined such as fusion of visible and thermal images and use of CNN networks. Also, existing challenges of thermal facial recognition and its applications in a real world will be pointed out.