Automatic Vision-Based Parking Slot Detection and Occupancy Classification

Parking guidance information (PGI) systems are used to provide information to drivers about the nearest parking lots and the number of vacant parking slots. Recently, vision-based solutions started to appear as a cost-effective alternative to standard PGI systems based on hardware sensors mounted on each parking slot. Vision-based systems provide information about parking occupancy based on images taken by a camera that is recording a parking lot. However, such systems are challenging to develop due to various possible viewpoints, weather conditions, and object occlusions. Most notably, they require manual labeling of parking slot locations in the input image which is sensitive to camera angle change, replacement, or maintenance. In this paper, the algorithm that performs Automatic Parking Slot Detection and Occupancy Classification (APSD-OC) solely on input images is proposed. Automatic parking slot detection is based on vehicle detections in a series of parking lot images upon which clustering is applied in bird's eye view to detect parking slots. Once the parking slots positions are determined in the input image, each detected parking slot is classified as occupied or vacant using a specifically trained ResNet34 deep classifier. The proposed approach is extensively evaluated on well-known publicly available datasets (PKLot and CNRPark+EXT), showing high efficiency in parking slot detection and robustness to the presence of illegal parking or passing vehicles. Trained classifier achieves high accuracy in parking slot occupancy classification.Comment: 39 pages, 8 figures, 9 table

One-shot lip-based biometric authentication: extending behavioral features with authentication phrase information

Lip-based biometric authentication (LBBA) is an authentication method based on a person's lip movements during speech in the form of video data captured by a camera sensor. LBBA can utilize both physical and behavioral characteristics of lip movements without requiring any additional sensory equipment apart from an RGB camera. State-of-the-art (SOTA) approaches use one-shot learning to train deep siamese neural networks which produce an embedding vector out of these features. Embeddings are further used to compute the similarity between an enrolled user and a user being authenticated. A flaw of these approaches is that they model behavioral features as style-of-speech without relation to what is being said. This makes the system vulnerable to video replay attacks of the client speaking any phrase. To solve this problem we propose a one-shot approach which models behavioral features to discriminate against what is being said in addition to style-of-speech. We achieve this by customizing the GRID dataset to obtain required triplets and training a siamese neural network based on 3D convolutions and recurrent neural network layers. A custom triplet loss for batch-wise hard-negative mining is proposed. Obtained results using an open-set protocol are 3.2% FAR and 3.8% FRR on the test set of the customized GRID dataset. Additional analysis of the results was done to quantify the influence and discriminatory power of behavioral and physical features for LBBA.Comment: 28 pages, 10 figures, 7 table

Multivariate statistical process monitoring

U industrijskoj proizvodnji prisutan je stalni rast zahtjeva, u prvom redu, u pogledu ekonomičnosti proizvodnje, kvalitete proizvoda, stupnja sigurnosti i zaštite okoliša. Put ka ispunjenju ovih zahtjeva vodi kroz uvođenje sve složenijih sustava automatskog upravljanja, što ima za posljedicu mjerenje sve većeg broja procesnih veličina i sve složenije mjerne sustave. Osnova za kvalitetno vođenje procesa je kvalitetno i pouzdano mjerenje procesnih veličina. Kvar na procesnoj opremi može značajno narušiti proizvodni proces, pa čak prouzrokovati ispad proizvodnje što rezultira visokim dodatnim troškovima. U ovom radu se analizira način automatskog otkrivanja kvara i identifikacije mjesta kvara u procesnoj mjernoj opremi, tj. senzorima. U ovom smislu mogu poslužiti različite statističke metode kojima se analiziraju podaci koji pristižu iz mjernog sustava. U radu se PCA i ICA metode koriste za modeliranje odnosa među procesnim veličinama, dok se za otkrivanje nastanka kvara koriste Hotellingova (T**2), I**2 i Q (SPE) statistike jer omogućuju otkrivanje neobičnih varijabilnosti unutar i izvan normalnog radnog područja procesa. Za identifikaciju mjesta (uzroka) kvara koriste se dijagrami doprinosa. Izvedeni algoritmi statističkog nadzora procesa temeljeni na PCA metodi i ICA metodi primijenjeni su na dva procesa različite složenosti te je uspoređena njihova sposobnost otkrivanja kvara.Demands regarding production efficiency, product quality, safety levels and environment protection are continuously increasing in the process industry. The way to accomplish these demands is to introduce ever more complex automatic control systems which require more process variables to be measured and more advanced measurement systems. Quality and reliable measurements of process variables are the basis for the quality process control. Process equipment failures can significantly deteriorate production process and even cause production outage, resulting in high additional costs. This paper analyzes automatic fault detection and identification of process measurement equipment, i.e. sensors. Different statistical methods can be used for this purpose in a way that continuously acquired measurements are analyzed by these methods. In this paper, PCA and ICA methods are used for relationship modelling which exists between process variables while Hotelling\u27s (T**2), I**2 and Q (SPE) statistics are used for fault detection because they provide an indication of unusual variability within and outside normal process workspace. Contribution plots are used for fault identification. The algorithms for the statistical process monitoring based on PCA and ICA methods are derived and applied to the two processes of different complexity. Apart from that, their fault detection ability is mutually compared

Adaptivna estimacija teško-mjerljivih procesnih veličina

There exist many problems regarding process control in the process industry since some of the important variables cannot be measured online. This problem can be significantly solved by estimating these difficult-tomeasure process variables. In doing so, the estimator is in fact an appropriate mathematical model of the process which, based on information about easy-to-measure process variables, estimates the current value of the difficultto-measure variable. Since processes are usually time-varying, the precision of the estimation based on the process model which is built on old data is decreasing over time. To avoid estimator accuracy degradation, model parameters should be continuously updated in order to track process behavior. There are a couple of methods available for updating model parameters depending on the type of process model. In this paper, PLSR process model is chosen as the basis of the difficult-to-measure process variable estimator while its parameters are updated in several ways – by the moving window method, recursive NIPALS algorithm, recursive kernel algorithm and Just-in-Time learning algorithm. Properties of these adaptive methods are explored on a simulated example. Additionally, the methods are analyzed in terms of computational load and memory requirements.Problemi s upravljanjem mnogih procesa u industriji vezani su s nemogućnošću on-line mjerenja nekih važnih procesnih veličina. Ovaj se problem može u značajnoj mjeri riješiti estimacijom ovih teško-mjerljivih procesnih veličina. Estimator je pri tome odgovarajući matematički model procesa koji na temelju informacije o ostalim (lako-mjerljivim) procesnim veličinama procjenjuje trenutni iznos teško-mjerljive veličine. Budući da su procesi po prirodi promjenjivi, točnost estimacije zasnovane na modelu procesa izgra.enog na starim podacima u pravilu opada s vremenom. Kako bi se ovo izbjeglo, parametre modela procesa je potrebno kontinuirano prepodešavati kako bi model što bolje opisivao (trenutno) vladanje procesa. Ovisno o tipu matematičkog modela, za prepodešavanje njegovih parametara na raspolaganju je više metoda. Kao osnova estimatora teško-mjerljive veličine u radu se koristi PLSR model procesa, dok se njegovi parametri prepodešavaju na više načina – metodom pomičnog prozora, rekurzivnim NIPALS algoritmom, rekurzivnim kernel algoritmom te Just-in-Time Learning metodom. Svojstva navedenih metoda adaptacije PLSR modela procesa ispitana su na odabranom primjeru. Nadalje, metode adaptacije su analizirane i s obzirom na računalnu i memorijsku zahtjevnost

Detection of dominant planar surfaces in disparity images based on random sampling

U ovom članku ispituje se praktična primjenjivost RANSAC-pristupa za detekciju ravnih površina na slikama dispariteta dobivenim pomoću stereo vizije. Težište istraživanja je primjena u interijerima, gdje je velik dio dominantnih površina jednolično obojen, što predstavlja poseban problem za stereo viziju. Ispitano je nekoliko jednostavnih modifikacija osnovnog RANSAC-algoritma s ciljem utvrđivanja koliko oni mogu poboljšati njegovu učinkovitost. Predložene su dvije jednostavne mjere točnosti rekonstrukcija ravnih površina. Provedeno je eksperimentalno istraživanje na slikama snimljenim sustavom stereo vizije montiranom na mobilnog robota koji se kretao hodnicima fakulteta.In this paper, the applicability of RANSAC-approach to planar surface detection in disparity images obtained by stereo vision is investigated. This study is specially focused on application in indoor environments, where many of the dominant surfaces are uniformly colored, which poses additional difficulties to stereo vision. Several simple modifications to the basic RANSAC-algorithm are examined and improvements achieved by these modifications are evaluated. Two simple performance measures for evaluating the accuracy of planar surface detection are proposed. An experimental study is performed using images acquired by a stereo vision system mounted on a mobile robot moving in an indoor environment

Naše iskustvo u endovaskularnom liječenju aneurizme abdominalne aorte talent stent-graftom

The aim of the study was to evaluate the safety and efficacy of endovascular treatment of abdominal aorta aneurysm (AAA) with Talent stent-graft (TSG). From October 1999 to February 2002, 18 patients (17 male and one female) aged 65-77, with AAA >5 cm in diameter, were treated by bifurcated Talent stent grafting. In all patients, postinterventional CT was performed 24 hours after stent grafting. The sutures were removed on the seventh to ninth day after the procedure, and the patients were released for home care. Regular spiral CT control evaluation was scheduled at 1, 3, 6, 9, 12 and 24 months after stent grafting. The mean follow up time was 11 months. Talent stent grafting procedure was initially successful in all patients. During regular controls, the TSGs were not occluded. In two (11%) patients, Talent stent grafting was preceded by preinterventional embolization. In five (27%) patients, control spiral CT after stent grafting showed a small endoleak. In these patients, systemic heparinization was interrupted and spiral CT was repeated after seven days. Repeat spiral CT after seven days showed absence of endoleak in four of five (80%) patients. A small endoleak in one patient disappeared spontaneously a month after TSG placement. The mean annual reduction in aneurysmal sac diameter was 7.3 (0 to 28) mm. TSG placement is a safe and efficient method of AAA treatment. In patients with accurately determined indication for the procedure, it is today considered a justified alternative to surgery.Cilj rada bio je procijeniti sigurnost i učinkovitost endovaskularnog liječenja aneurizme abdominalne aorte (AAA) talent stent-graftom (TSG). Od listopada 1999. do veljače 2002. godine 18 bolesnika (17 muškaraca i jedna žena) životne dobi između 65 i 77 godina s AAA promjera >5 cm liječeno je postavljanjem bifurkacijskog TSG. U svih bolesnika poslijeintervencijski CT rađen je 24 sata nakon intervencije. Sedmog do devetog poslije intervencijskog dana skinuti su šavi i bolesnici su otpušteni na kućnu njegu. Redoviti kontrolni pregledi provedeni se spiralnim CT-om, i to 1., 3., 6., 9., 12. i 24. mjeseca nakon postavljanja TSG. Srednje vrijeme praćenja iznosilo je 11 mjeseci. U svih bolesnika postignut je primarni uspjeh, a na kontrolnim pregledima TSG je bio prohodan. U dvoje (11%) bolesnika prije postavljanja TSG napravljene su prije intervencijske embolizacije. U petoro (27%) bolesnika na poslijeintervencijskom CT-u bilo je vidljivo manje endopropuštanje. U tih bolesnika ukinuta je sistemska heparinizacija i na spiralnom CT-u ponovljenom nakon sedam dana u četvoro od pet (80%) bolesnika endopropuštanja više nije bilo. U jednog je bolesnika manje endopropuštanje spontano prestalo mjesec dana nakon postavljanja TSG. Srednje smanjenje promjera aneurizmatske vreće iznosilo je 7,3 mm (0-28 mm) na godinu. Postavljanje TSG predstavlja sigurnu i učinkovitu metodu liječenja AAA. U bolesnika s pravilno postavljenom indikacijom danas se opravdano smatra alternativnom metodom kirurškom liječenju

Analytical performance evaluation of time-hopping Pulse Position Amplitude Modulation IR-UWB systems under multi user interference

U ovom radu je korištena metoda karakteristične funkcije (CF) za izvod učestalosti pogreške simbola (SER) u impulsnim ultra-širokopojasnim radio sustavima s vremenskim poskakivanjem (TH-IR-UWB) i Pulsno Pozicijsko Amplitudnom Modulacijom (PPAM) u prisustvu više-korisničke smetnje (MUI). Dobiveni izraz je potvrđen Monte-Karlo simulacijom i uspoređen s ortogonalnom Pulsno Pozicijskom Modulacijom (PPM). Nadalje, analitički rezultati su uspoređeni s Gaussovom aproksimacijom (GA) više-korisničke smetnje, za koju je pokazano da je neprecizna za srednje i velike odnose signal-šum (SNR). Još je pokazano da PPAM nadmašuje PPM za sve odnose SNR. Na kraju je analiziran utjecaj različitih parametara modulacije na svojstva PPAM.In this paper, the characteristic function (CF) method is used to derive symbol error rate (SER) expression for time-hopping impulse radio ultra–wideband (TH-IR-UWB) systems with Pulse Position Amplitude Modulation (PPAM) scheme in the presence of multi-user interference (MUI). The derived expression is validated with Monte-Carlo simulation and compared with orthogonal Pulse Position Modulation (PPM). Moreover, the analytical results are compared with Gaussian approximation (GA) of MUI which is shown to be inaccurate for medium and large signal-to-noise ratio (SNR). It is also shown that PPAM scheme outperforms PPM scheme for all SNR. At the end the influence of different system parameters on the PPAM performance is analyzed

On-line predobradba podataka u izgradnji adaptivnog modela procesa na temelju pogonskih podataka

Process variables which are concerned with the quality of final product cannot often be measured by a sensor. The alternative procedure is the estimation of these difficult-to-measure process variables for which it is necessary to have an appropriate process model. Process model building, based on plant data taken from the process database, is usually the most cost-effective way to obtain a process model. Since the quality of the built model depends heavily on the modelling data informativity, preprocessing of the available measured data is an important step in such process modelling. Processes are usually time-varying and non-stationary, so that the precision of the estimation based on process model with constant parameters degrades over time. Because of that, model parameters have to be updated online. However, in order to successfully keep the precision of the estimation, it is important to use the samples which do not contain errors in the parameter updating procedure which requires a quality online data preprocessing. The online data preprocessing and online model parameter updating are discussed and presented on two examples and the influence of data preprocessing on adaptive process model quality is analyzed.Vrlo često važne procesne veličine koje su povezane s kvalitetom izlaznog proizvoda nije moguće mjeriti senzorom. Alternativni postupak je procjenjivanje iznosa ovih teško-mjerljivih veličina, za što je potreban odgovarajući matematički model procesa. Izgradnja modela procesa na pogonskim podacima preuzetim iz procesne baze podataka potencijalno je najjeftiniji način iznalaženja modela. Budući da kakvoća izgrađenog modela uvelike ovisi o informativnosti raspoloživih mjernih podataka, predobrada mjernih podataka je važan korak u izgradnji modela procesa na temelju pogonskih podataka. Budući da su procesi najčešće vremenski promjenjivi i nestacionarni, točnost procjene teško-mjerljive veličine modelom procesa s konstantnim parametrima opada s vremenom. Zbog toga je potrebno prepodešavati parametre modela "online". Prilikom prepodešavanja parametara modela, kako bi se uspješno održavala točnost procjene, potrebno je koristiti uzorke koji su bez grešaka što zahtijeva kvalitetnu online predobradbu ovih uzoraka. Predobradba podataka na online način kao i online prepodešavanje parametara modela prikazani su na dva primjera te je provedena analiza utjecaja predobradbe podataka na svojstva adaptivnog modela procesa

Metode modeliranja na pogonskim podacima za razvoj soft-senzora

There has been an increased use of soft-sensors in process industry in recent years. These soft-sensors are computer programs that are used as a relatively cheap alternative to hardware sensors. Since process variables, which are concerned with final product quality, cannot always be measured by hardware sensors, designing the appropriate soft-sensor can be an interesting solution. Additionally, a soft-sensor can be used as a backup sensor, when the hardware sensor is in fault or removed due to maintenance or replacement. Soft-sensor is based on the mathematical model of the process. Since industrial processes are generally quite complex, a theoretical modeling approach is often impractical, expensive or sometimes even impossible. Therefore, process model building is based on measured data. This approach significantly gets complicated if only plant data, taken from the process database, are available. In this paper the most popular methods for plant data-based modeling that appeared in the last two decades are summarized and briefly explained. Apart from giving a short survey of the most important papers, tips about choosing the appropriate methodology for process model building are also provided.U posljedne vrijeme moguće je uočiti povećano korištenje soft-senzora u procesnoj industriji. Soft-senzori su računalni programi koji su jeftina alternativa hardverskim senzorima. Budući da su procesne veličine povezane s kvalitetomizlaznog proizvoda često nemjerljive, razvoj soft-senzora može biti zanimljivo rješenje. Nadalje, soft-senzor se može koristiti kao zamjena za hardverski senzor kada je on u kvaru ili nedostaje uslijed održavanja ili zamjene. Soft-senzor se temelji na odgovarajućem matematičkom modelu procesa. Kako su industrijski procesi najčešće vrlo složeni, teorijski pristup modeliranju procesa često je nepraktičan, vrlo skup, a ponekad čak i nemoguć. Iz tog razloga, izgradnja modela procesa često se temelji na mjernim podacima. Ovaj pristup se značajno usložnjava ako su na rapolaganju samo pogonski podaci, preuzeti iz procesne baze podataka. U ovom radu su ukratko opisane postojeće metode za modeliranje procesa na temelju pogonskih podataka koje su se pojavile u posljednja dva desetljeća. Osim toga, ukratko su izloženi najznačajniji radovi, a dane su i smjernice za odabir pogodne metode za izgradnju modela procesa