Trade-off between accuracy and speed for pedestrian detection using HOG feature

Kim, Soojin; Cho, Kyeongsoon

doi:10.1109/icce-berlin.2013.6698033

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…Machine learning algorithms focus on feature extraction and classifiers [92]. For feature extraction, techniques such as Histogram of Oriented Gradients [93][94][95][96][97][98][99][100], Local Binary Pattern [101][102][103][104][105][106][107], Deformable Part Model [108][109][110][111][112][113], and Aggregate Channel Feature (ACF) [114][115][116][117][118] are included. On the other hand, methods such as Support Vector Machine (SVM) [94,105,[119][120][121][122], Decision Tree [123][124][125][126], Random Forest (RF) [127][128][129][130][131][132] and Ada-Boost [81,119,133,134] are used for ...…”

Section: Object Detection and Classificationmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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Section: Object Detection and Classificationmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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“…Feature extraction is the initial stage of the species recognition, and it affects the accuracy significantly. In the previous study, the gray-level co-occurrence matrix (GLCM) [17] and the histogram of oriented gradient (HOG) [18] were often used to extract the image features [19,20], but these features are weak in robustness. Later, the neural networks were adopted to extract the features, which improved the robustness and obtained better results.…”

Section: Feature Extractingmentioning

confidence: 99%

An Improved Wood Recognition Method Based on the One-Class Algorithm

Sun

et al. 2022

Forests

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Wood recognition is necessary for work in the wood trade activities. The advantage of the one-class wood classification method is more generalization, and it only needs positive samples and does not need negative samples in the training phase, so it is suitable for rare wood species inspection. This paper proposed an improved method based on the one-class support vector machine (OCSVM) for wood species recognition. It uses cross-section images acquired with a magnifying glass, which uses a pre-trained VGG16 model for feature extraction, a normal distribution test for key features filtering, and OCSVM to determine the wood species. The results showed that the approach achieved a mean recall of 0.842 for both positive and negative samples, which indicates this method has good performance for wood recognition. In a negative public dataset, the negative recall reached as high as 0.989, which showed that this method has good generalization.

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“…La detección de peatones, sobre imágenes monoculares, usando visión por computador e inteligencia artificial, es un área de interés para la comunidad científica por el sinnúmero de aplicaciones; uno de los principales campos son los sistemas de asistencia a la conducción, y en particular el desarrollo de sistemas para la detección de peatones (SDP), donde existen varios retos a superar [1], [2], [3], [4], [5], [6], [7].…”

Section: Introductionunclassified

Generación de regiones de interés con potencial de contener peatones mediante búsqueda focalizada usando visión monocular

Flores-Calero

Quinga

Onofa

et al. 2019

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Este artículo presenta el desarrollo de un algoritmo para la generación de regiones de interés con alto potencial de contener peatones sobre imágenes monoculares. Para la generación de estas regiones se ha construido un algoritmo de generación de hiperplanos de búsqueda en función de la carretera junto con la generación de ventanas aleatorias, sobre ésta zona con una variación de la técnica desplazamiento de ventana piramidal; luego se realiza el pre-procesamiento usando los filtros de gradientes vertical y horizontal. Para comprobar que la región es un posible peatón, se parte de dos hipótesis respecto a la figura humana, la componente vertical es mayor que la horizontal y la fuerte simetría vertical. Mediante este proceso se obtiene un conjunto reducido y óptimo de regiones en el intervalo de entre 2 y 25 metros al frente de la cámara. Los resultados experimentales, desarrollados sobre las bases de datos presentes en el estado del arte, muestran que se tiene una tasa del 91% de ventanas válidas con respecto al total de ventanas verdaderas, a 25.38 fotogramas por segundo.

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Trade-off between accuracy and speed for pedestrian detection using HOG feature

Cited by 12 publications

References 8 publications

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

An Improved Wood Recognition Method Based on the One-Class Algorithm

Generación de regiones de interés con potencial de contener peatones mediante búsqueda focalizada usando visión monocular

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