Dynamic loss balancing and sequential enhancement for road-safety assessment and traffic scene classification

Road-safety inspection is an indispensable instrument for reducing road-accident fatalities contributed to road infrastructure. Recent work formalizes road-safety assessment in terms of carefully selected risk factors that are also known as road-safety attributes. In current practice, these attributes are manually annotated in geo-referenced monocular video for each road segment. We propose to reduce dependency on tedious human labor by automating recognition with a two-stage neural architecture. The first stage predicts more than forty road-safety attributes by observing a local spatio-temporal context. Our design leverages an efficient convolutional pipeline, which benefits from pre-training on semantic segmentation of street scenes. The second stage enhances predictions through sequential integration across a larger temporal window. Our design leverages per-attribute instances of a lightweight bidirectional LSTM architecture. Both stages alleviate extreme class imbalance by incorporating a multi-task variant of recall-based dynamic loss weighting. We perform experiments on the iRAP-BH dataset, which involves fully labeled geo-referenced video along 2,300 km of public roads in Bosnia and Herzegovina. We also validate our approach by comparing it with the related work on two road-scene classification datasets from the literature: Honda Scenes and FM3m. Experimental evaluation confirms the value of our contributions on all three datasets.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Detecting Lexical Transfer Errors of Second Language Learners

Automatizirano ispravljanje pogrešaka važan je zadatak obrade prirodnog jezika. Pogreške leksičkog transfera učestala su kod učenika stranih jezika. Najčešći je uzrok transfera višeznačnost riječi. Cilj ovog rada bio je osmisliti model koji otkriva i ispravlja takve pogreške za dva tipa jezičnih relacija: pridjevsko-imeničke i glagolsko-objektne. Implementirana su dva različita modela, od kojih drugi koristi metode nadziranog strojnog učenja, preciznije algoritam regresije pomoću stroja s potpornim vektorima. Jezik implementacije je Python. Ovaj projekt nastao je u suradnji s kineskim sveučilištem Xi'an Jiaotong-Liverpool University, te se oni ustupili skup podataka koji se jednim dijelom koristi u ovome radu.Automated error correction is an important task of natural language processing. Lexical transfer errors are common with L2-learners. The biggest cause of transfer is word polysemy. The goal of this paper was to come up with a model that can detect and correct such errors for two language relation types: adjective-noun and verb-object. Two different models were implemented, the second of which uses supervised learning methods, more precisely the support vector regression algorithm. The model was implemented in Python. This project is carried out in cooperation with Xi'an Jiaotong-Liverpool University, China, who also make the dataset available, a part of which was used in this paper

Metric embeddings for representing persons in images

Metrička ugrađivanja vrlo su važan sastojak primjena u kojima ostvarujemo korespondenciju slika iste osobe u različitim trenutcima. Posebno su zanimljive primjene vezane uz praćenje i ponovnu identifikaciju osoba. U posljednje vrijeme, veliki uspjeh u tom području ostvaruju duboki modeli učeni različitim metričkim gubitcima. U okviru rada, proučeni su i ukratko opisani postojeći pristupe za učenje metričkih ugrađivanja. U okviru rada, uhodani su postupci nadziranog učenja prikladnih dubokih modela. Napravljena je usporedba utjecaja različitih metričkih ugrađivanja i različitih postupaka detekcije objekata na performanse postupka praćenja. Prikazani su rezultati eksperimenata i predloženi su pravci budućeg razvoja.Metric embeddings are very important when we need to achieve a correspondence between images of the same person at different moments in time. Applications in multiple person tracking and person re-identification are particularly interesting. As of late, deep models trained with different metric losses have been very successful in that area. In this thesis, we use supervised learning to train different deep models for this task. We also compare the effects of different metric embeddings and different object detection procedures on the performance of the tracking procedure. We show the experiment results and suggest promising directions for future work

Metric embeddings for representing persons in images

Metrička ugrađivanja vrlo su važan sastojak primjena u kojima ostvarujemo korespondenciju slika iste osobe u različitim trenutcima. Posebno su zanimljive primjene vezane uz praćenje i ponovnu identifikaciju osoba. U posljednje vrijeme, veliki uspjeh u tom području ostvaruju duboki modeli učeni različitim metričkim gubitcima. U okviru rada, proučeni su i ukratko opisani postojeći pristupe za učenje metričkih ugrađivanja. U okviru rada, uhodani su postupci nadziranog učenja prikladnih dubokih modela. Napravljena je usporedba utjecaja različitih metričkih ugrađivanja i različitih postupaka detekcije objekata na performanse postupka praćenja. Prikazani su rezultati eksperimenata i predloženi su pravci budućeg razvoja.Metric embeddings are very important when we need to achieve a correspondence between images of the same person at different moments in time. Applications in multiple person tracking and person re-identification are particularly interesting. As of late, deep models trained with different metric losses have been very successful in that area. In this thesis, we use supervised learning to train different deep models for this task. We also compare the effects of different metric embeddings and different object detection procedures on the performance of the tracking procedure. We show the experiment results and suggest promising directions for future work

Detecting Lexical Transfer Errors of Second Language Learners

Automatizirano ispravljanje pogrešaka važan je zadatak obrade prirodnog jezika. Pogreške leksičkog transfera učestala su kod učenika stranih jezika. Najčešći je uzrok transfera višeznačnost riječi. Cilj ovog rada bio je osmisliti model koji otkriva i ispravlja takve pogreške za dva tipa jezičnih relacija: pridjevsko-imeničke i glagolsko-objektne. Implementirana su dva različita modela, od kojih drugi koristi metode nadziranog strojnog učenja, preciznije algoritam regresije pomoću stroja s potpornim vektorima. Jezik implementacije je Python. Ovaj projekt nastao je u suradnji s kineskim sveučilištem Xi'an Jiaotong-Liverpool University, te se oni ustupili skup podataka koji se jednim dijelom koristi u ovome radu.Automated error correction is an important task of natural language processing. Lexical transfer errors are common with L2-learners. The biggest cause of transfer is word polysemy. The goal of this paper was to come up with a model that can detect and correct such errors for two language relation types: adjective-noun and verb-object. Two different models were implemented, the second of which uses supervised learning methods, more precisely the support vector regression algorithm. The model was implemented in Python. This project is carried out in cooperation with Xi'an Jiaotong-Liverpool University, China, who also make the dataset available, a part of which was used in this paper

Detecting Lexical Transfer Errors of Second Language Learners

Automatizirano ispravljanje pogrešaka važan je zadatak obrade prirodnog jezika. Pogreške leksičkog transfera učestala su kod učenika stranih jezika. Najčešći je uzrok transfera višeznačnost riječi. Cilj ovog rada bio je osmisliti model koji otkriva i ispravlja takve pogreške za dva tipa jezičnih relacija: pridjevsko-imeničke i glagolsko-objektne. Implementirana su dva različita modela, od kojih drugi koristi metode nadziranog strojnog učenja, preciznije algoritam regresije pomoću stroja s potpornim vektorima. Jezik implementacije je Python. Ovaj projekt nastao je u suradnji s kineskim sveučilištem Xi'an Jiaotong-Liverpool University, te se oni ustupili skup podataka koji se jednim dijelom koristi u ovome radu.Automated error correction is an important task of natural language processing. Lexical transfer errors are common with L2-learners. The biggest cause of transfer is word polysemy. The goal of this paper was to come up with a model that can detect and correct such errors for two language relation types: adjective-noun and verb-object. Two different models were implemented, the second of which uses supervised learning methods, more precisely the support vector regression algorithm. The model was implemented in Python. This project is carried out in cooperation with Xi'an Jiaotong-Liverpool University, China, who also make the dataset available, a part of which was used in this paper

Metric embeddings for representing persons in images

Metrička ugrađivanja vrlo su važan sastojak primjena u kojima ostvarujemo korespondenciju slika iste osobe u različitim trenutcima. Posebno su zanimljive primjene vezane uz praćenje i ponovnu identifikaciju osoba. U posljednje vrijeme, veliki uspjeh u tom području ostvaruju duboki modeli učeni različitim metričkim gubitcima. U okviru rada, proučeni su i ukratko opisani postojeći pristupe za učenje metričkih ugrađivanja. U okviru rada, uhodani su postupci nadziranog učenja prikladnih dubokih modela. Napravljena je usporedba utjecaja različitih metričkih ugrađivanja i različitih postupaka detekcije objekata na performanse postupka praćenja. Prikazani su rezultati eksperimenata i predloženi su pravci budućeg razvoja.Metric embeddings are very important when we need to achieve a correspondence between images of the same person at different moments in time. Applications in multiple person tracking and person re-identification are particularly interesting. As of late, deep models trained with different metric losses have been very successful in that area. In this thesis, we use supervised learning to train different deep models for this task. We also compare the effects of different metric embeddings and different object detection procedures on the performance of the tracking procedure. We show the experiment results and suggest promising directions for future work

Deep Learning Approach for Object Classification on Raw and Reconstructed GBSAR Data

The availability of low-cost microwave components today enables the development of various high-frequency sensors and radars, including Ground-based Synthetic Aperture Radar (GBSAR) systems. Similar to optical images, radar images generated by applying a reconstruction algorithm on raw GBSAR data can also be used in object classification. The reconstruction algorithm provides an interpretable representation of the observed scene, but may also negatively influence the integrity of obtained raw data due to applied approximations. In order to quantify this effect, we compare the results of a conventional computer vision architecture, ResNet18, trained on reconstructed images versus one trained on raw data. In this process, we focus on the task of multi-label classification and describe the crucial architectural modifications that are necessary to process raw data successfully. The experiments are performed on a novel multi-object dataset RealSAR obtained using a newly developed 24 GHz (GBSAR) system where the radar images in the dataset are reconstructed using the Omega-k algorithm applied to raw data. Experimental results show that the model trained on raw data consistently outperforms the image-based model. We provide a thorough analysis of both approaches across hyperparameters related to model pretraining and the size of the training dataset. This, in conclusion, shows how processing raw data provides overall better classification accuracy, it is inherently faster since there is no need for image reconstruction and it is therefore useful tool in industrial GBSAR applications where processing speed is critical