Long H Phạm | HCMC_International University Vietnam (original) (raw)
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Papers by Long H Phạm
In the field of traffic monitoring systems, shadows are the main causes of errors in computer vis... more In the field of traffic monitoring systems, shadows are the main causes of errors in computer vision-based vehicle detection and classification. A great number of research have been carried out to detect and remove shadows. However, these research works only focused on solving shadow problems in daytime traffic scenes. Up to now, far too little attention has been paid to the problem caused by vehicles' reflections in rainy conditions. Unlike shadows in the daytime, which are homogeneous gray shades, reflection shadows are inhomogeneous regions of different colors. This characteristic makes reflections harder to detect and remove. Therefore, in this paper, we aim to develop a reflection detection and removal method from single images or video. Reflections are detected by determining a combination of L and B channels from LAB color space and H channel from HSV color space. The reflection removal method is performed by determining the optimal intensity of reflected areas so that they match with neighbor regions. The advantage of our method is that all reflected areas are removed without affecting vehicles' textures or details.
In the area of intelligent traffic system, vehicle classification has emerged as a significant fi... more In the area of intelligent traffic system, vehicle classification has emerged as a significant field of study. There has been a considerable amount of research to accommodate this subject since the 90s. So far almost studies have been only carried out in developed countries where traffic infrastructures are built around automobiles but in developing countries, motorbikes are dominant. The traffic of two-wheeled motorized vehicles is the main reason causing traffic chaos in developing-country cities. In this paper, a new algorithm to detect and classify vehicles is proposed. The algorithm aims to identify and count vehicles into 3 common groups: light vehicle (motorbike + bike), medium vehicle (small car + minivan), heavy vehicle (truck + bus). Our approach has demonstrated more accurate results than previous approaches.
Improved Vehicles Detection & Classification Algorithm for Traffic Surveillance System, Oct 2014
Vehicles detection and classification are the most popular subjects in the computer vision resear... more Vehicles detection and classification are the most popular subjects in the computer vision researching field, and also are the most important parts in any traffic monitoring or surveillance system. Although there has been a considerable amount of ideas to accommodate this problem since the 90s, many problems are still unresolved due to the complexity of traffic systems and the variety of vehicles. This paper is a work- in-process that proposes a new approach to detect and classify vehicles based on the traffic system in Vietnam. The main goal of this method is to group vehicles into 2 main classes, which are 2- wheeled and 4-wheeled vehicles, based on low-level traffic parameters in urban areas.
Vehicles detection and classification are the most popular subjects in the computer vision resear... more Vehicles detection and classification are the most popular subjects in the computer vision researching field, and also are the most important parts in any traffic monitoring or surveillance system. Although there has been a considerable amount of ideas to accommodate this problem since the 90s, many problems are still unresolved due to the complexity of traffic systems and the variety of vehicles. This paper is a work-in-process that proposes a new approach to detect and classify vehicles based on the traffic system in Vietnam. The main goal of this method is to group vehicles into 2 main classes, which are 2-wheeled and 4-wheeled vehicles, based on low-level traffic parameters in urban areas.
In the field of traffic monitoring systems, shadows are the main causes of errors in computer vis... more In the field of traffic monitoring systems, shadows are the main causes of errors in computer vision-based vehicle detection and classification. A great number of research have been carried out to detect and remove shadows. However, these research works only focused on solving shadow problems in daytime traffic scenes. Up to now, far too little attention has been paid to the problem caused by vehicles' reflections in rainy conditions. Unlike shadows in the daytime, which are homogeneous gray shades, reflection shadows are inhomogeneous regions of different colors. This characteristic makes reflections harder to detect and remove. Therefore, in this paper, we aim to develop a reflection detection and removal method from single images or video. Reflections are detected by determining a combination of L and B channels from LAB color space and H channel from HSV color space. The reflection removal method is performed by determining the optimal intensity of reflected areas so that they match with neighbor regions. The advantage of our method is that all reflected areas are removed without affecting vehicles' textures or details.
In the area of intelligent traffic system, vehicle classification has emerged as a significant fi... more In the area of intelligent traffic system, vehicle classification has emerged as a significant field of study. There has been a considerable amount of research to accommodate this subject since the 90s. So far almost studies have been only carried out in developed countries where traffic infrastructures are built around automobiles but in developing countries, motorbikes are dominant. The traffic of two-wheeled motorized vehicles is the main reason causing traffic chaos in developing-country cities. In this paper, a new algorithm to detect and classify vehicles is proposed. The algorithm aims to identify and count vehicles into 3 common groups: light vehicle (motorbike + bike), medium vehicle (small car + minivan), heavy vehicle (truck + bus). Our approach has demonstrated more accurate results than previous approaches.
Improved Vehicles Detection & Classification Algorithm for Traffic Surveillance System, Oct 2014
Vehicles detection and classification are the most popular subjects in the computer vision resear... more Vehicles detection and classification are the most popular subjects in the computer vision researching field, and also are the most important parts in any traffic monitoring or surveillance system. Although there has been a considerable amount of ideas to accommodate this problem since the 90s, many problems are still unresolved due to the complexity of traffic systems and the variety of vehicles. This paper is a work- in-process that proposes a new approach to detect and classify vehicles based on the traffic system in Vietnam. The main goal of this method is to group vehicles into 2 main classes, which are 2- wheeled and 4-wheeled vehicles, based on low-level traffic parameters in urban areas.
Vehicles detection and classification are the most popular subjects in the computer vision resear... more Vehicles detection and classification are the most popular subjects in the computer vision researching field, and also are the most important parts in any traffic monitoring or surveillance system. Although there has been a considerable amount of ideas to accommodate this problem since the 90s, many problems are still unresolved due to the complexity of traffic systems and the variety of vehicles. This paper is a work-in-process that proposes a new approach to detect and classify vehicles based on the traffic system in Vietnam. The main goal of this method is to group vehicles into 2 main classes, which are 2-wheeled and 4-wheeled vehicles, based on low-level traffic parameters in urban areas.