An ROIs based pedestrian detection system for single images

Shou, Na; Peng, Hong; Wang, Hui; Meng, Limin; Du, Ke-Lin

doi:10.1109/cisp.2012.6469927

Cited by 9 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data preparation conducted by the on-field unit should be lightweight to minimize the computation time because of the limited energy resources. Several simple image processing activities, i.e., grayscale conversion, image enhance- [49,53] Filter application [49] Enhancement [48] Integration Model partition [114,115,116] Model partition [114,115,116] Discretization [54] Reduction Feature extraction [54] Feature reduction [54,55] ,…”

Section: Non-offloading Data Preparationmentioning

confidence: 99%

See 1 more Smart Citation

Mobile computing and communications-driven fog-assisted disaster evacuation techniques for context-aware guidance support: A survey

Kurniawan

Asyhari

et al. 2021

Computer Communications

View full text Add to dashboard Cite

Section: Non-offloading Data Preparationmentioning

confidence: 99%

“…Object localization [117,50] Dimensionality reduction [55] Compression [49,53] Instance reduction [114,115,116] Grayscale conversion [48] Note.…”

Section: Non-offloading Data Preparationmentioning

confidence: 99%

Mobile computing and communications-driven fog-assisted disaster evacuation techniques for context-aware guidance support: A survey

Kurniawan

Asyhari

et al. 2021

Computer Communications

View full text Add to dashboard Cite

“…Así, Shou et al [10] han utilizado la técnica Fuzzy C-means para segmentar la imagen y generar un conjunto de ROIs con alto potencial de contener peatones. En Flores-Calero, Quinga, Onofa y Gallardo, Generación de regiones de interés con potencial de contener peatones mediante búsqueda focalizada usando visión monocular Enero -Junio 2019 http://dx.doi.org/10.33210/ca.v8i1.178 otro caso, para detectar peatones a distintas distancias se utiliza SWP en [11].…”

Section: Estado Del Arteunclassified

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

Flores-Calero

Quinga

Onofa

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

“…Approaches in the former category aim to localize structural features that are invariant to the aforementioned diversities and use them to detect ROI. Popular approaches under this category include, background subtraction guided salient area detection method [24], SIFT based region localization methods [9], HoG based methods [26], and salience guided methods [1]. In contrast, the top-down approaches usually start building the scene model using contextual or scene-level information [20].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Localization of region of interest in surveillance scene

Sekh

Dogra

Kar

et al. 2016

Multimed Tools Appl

View full text Add to dashboard Cite

In this paper, we present a method for autonomously detecting and extracting region(s)-of-interest (ROI) from surveillance videos using trajectorybased analysis. Our approach, localizes ROI in a stochastic manner using correlated probability density functions that model motion dynamics of multiple moving targets. The motion dynamics model is built by analyzing trajectories of multiple moving targets and associating importance to regions in the scene. The importance of each region is estimated as a function of the total time spent by multiple targets, their instantaneous velocity and direction of movement whilst passing through that region. We systematically validate our model and benchmark our technique against competing baselines through extensive experimentation using public datasets such as CAVIAR, ViSOR, and CUHK as well as a scenario-specific in-house surveillance dataset. Results obtained have demonstrated the superiority of the proposed technique against a few popular existing state-of-the-art techniques.

show abstract

An ROIs based pedestrian detection system for single images

Cited by 9 publications

References 9 publications

Mobile computing and communications-driven fog-assisted disaster evacuation techniques for context-aware guidance support: A survey

Mobile computing and communications-driven fog-assisted disaster evacuation techniques for context-aware guidance support: A survey

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

Localization of region of interest in surveillance scene

Contact Info

Product

Resources

About