Invariant Color Model-Based Shadow Removal in Traffic Image and a New Metric for Evaluating the Performance of Shadow Removal Methods

Soh, Young Sung; Lee, Hwanju; Wang, Yakun

doi:10.1007/11801603_58

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where subscripts S and F refer to shadow and foreground, and F TP represents the number of foreground pixels minus the number of foreground pixels wrongly classified as shadow. We also report the more intuitive weighted metric g proposed by Soh et al [17]:…”

Section: Quantitative Performance Analysismentioning

confidence: 99%

Optimization of Color-based Foreground / Background Segmentation for Outdoor Scenes

St-Laurent¹,

Prevost²,

Maldague³

2012

Signal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imaging

View full text Add to dashboard Cite

Performing foreground / background segmentation in outdoor scene is very challenging. Starting from a stateof-the-art color-based approach [8], we propose three improvements: the use of the YCoCg c o l o r s p a c e , a spherical association volume, and a cast shadows management approach. U s i n g i m a g e s e q u e n c e s w i t h ground truth at pixel-level, we quantitatively measured the performances of the proposed algorithm and demonstrated that it leads to a reduced processing time with improved detection accuracy. We also introduce a new public dataset of outdoor videos with ground truth.

show abstract

Section: Quantitative Performance Analysismentioning

confidence: 99%

Optimization of Color-based Foreground / Background Segmentation for Outdoor Scenes

St-Laurent¹,

Prevost²,

Maldague³

2012

Signal Processing, Pattern Recognition and Applications / 779: Computer Graphics and Imaging

View full text Add to dashboard Cite

show abstract

“…They are parsed into frame sequences. Because there is shadow in every frame of the videos, pre-processing is carried out to remove shadow from each frame by the shadow removal algorithm of Soh et al [38]. The core of this algorithm is the C1C2C3 transformation, which has proved to be a good invariant colour model to remove shadow.…”

Section: On-demand Experimentsmentioning

confidence: 99%

Real-Time On-Demand Motion Video Change Detection in the Sensor Web Environment

Chen

et al. 2011

The Computer Journal

View full text Add to dashboard Cite

Detecting motion-based video change, such as different types of motion video or applications using different change detection algorithms in a Web system, is difficult. This paper designs and implements architecture for a real-time or near real-time on-demand motion video change detection system using the Sensor Observation Service (SOS) and the Web Processing Service (WPS) in the Sensor Web environment. Real-time or near real-time includes sensors that obtain motion video data, SOS provides motion video data to WPS and WPS processes this data. Three solution methods are introduced: the GetObservation operation of SOS by transaction, dynamical interaction between SOS and WPS, and WPS real-time or near real-time processing. On-demand means that a developer can choose different motion video change detection algorithms under different applications or different conditions. For this purpose, a flexible, standards-based and service-oriented WPS architecture is designed, which consists of three layers: the WPS interface layer, the field interface layer and the implementation layer. To test the proposed approach, a video change detection case of monitoring a road situation is shown, which was a demonstration for Open Geospatial Consortium Web Service phase 7. The results demonstrate that the proposed approach is feasible.

show abstract

Invariant Color Model-Based Shadow Removal in Traffic Image and a New Metric for Evaluating the Performance of Shadow Removal Methods

Cited by 2 publications

References 0 publications

Optimization of Color-based Foreground / Background Segmentation for Outdoor Scenes

Optimization of Color-based Foreground / Background Segmentation for Outdoor Scenes

Real-Time On-Demand Motion Video Change Detection in the Sensor Web Environment

Contact Info

Product

Resources

About