DetectionOfSFO

 

Research  

 

GTI Data   

 

Open databases created and software developed by the GTI and supplemental material to papers.  

 

Databases  

Ficosa (2024):  The FNTVD dataset has been generated using the Ficosa's recording car.
MATDAT (2023):  More than 90K labeled images of martial arts tricking.
SEAW – DATASET (2022): 3 stereoscopic contents in 4K resolution at 30 fps.
UPM-GTI-Face dataset (2022): 11 different subjects captured in 4K, under 2 scenarios, and 2 face mask conditions.
LaSoDa (2022): 60 annotated images from soccer matches in five stadiums with different characteristics and light conditions.
PIROPO Database (2021):People in Indoor ROoms with Perspective and Omnidirectional cameras.
EVENT-CLASS (2021): High-quality 360-degree videos in the context of tele-education.
Parking Lot Occupancy Database (2020)
Nighttime Vehicle Detection database (NVD) (2019)
Hand gesture dataset (2019): Multi-modal Leap Motion dataset for Hand Gesture Recognition.
ViCoCoS-3D (2016): VideoConference Common Scenes in 3D.
LASIESTA database (2016): More than 20 sequences to test moving object detection and tracking algorithms.
Hand gesture database (2015): Hand-gesture database composed by high-resolution color images acquired with the Senz3D sensor.
HRRFaceD database (2014):Face database composed by high resolution images acquired with Microsoft Kinect 2 (second generation).
Lab database (2012): Set of 6 sequences to test moving object detection strategies.
Vehicle image database (2012): More than 7000 images of vehicles and roads.           

 

Software  

Solving the Team Orienteering Problem with Transformers (2023)
Solving Routing Problems for Multiple Cooperative Unmanned Aerial Vehicles using Transformer Networks (2023)
Vision Transformers and Traditional Convolutional Neural Networks for Face Recognition Tasks (2023)
Faster GSAC-DNN (2023): A Deep Learning Approach to Nighttime Vehicle Detection Using a Fast Grid of Spatial Aware Classifiers.
SETForSeQ (2020): Subjective Evaluation Tool for Foreground Segmentation Quality. 
SMV Player for Oculus Rift (2016)
Bag-D3P (2016): Face recognition using depth information. 
TSLAB (2015): Tool for Semiautomatic LABeling.    

   

Supplementary material  

Soccer line mark segmentation and classification with stochastic watershed transform (2022)
A fully automatic method for segmentation of soccer playing fields (2022)
Grass band detection in soccer images for improved image registration (2022)
Evaluating the Influence of the HMD, Usability, and Fatigue in 360VR Video Quality Assessments (2020)
Automatic soccer field of play registration (2020)   
Augmented reality tool for the situational awareness improvement of UAV operators (2017)
Detection of static moving objects using multiple nonparametric background-foreground models on a Finite State Machine (2015)
Real-time nonparametric background subtraction with tracking-based foreground update (2015)  
Camera localization using trajectories and maps (2014)

 

                                                                                                                                                                                                                             
 
                                                                   
 
                                                                                                                                                             
 
      

 

 

Detection of stacionary foreground objects using multiple nonparametric background-foreground models on a Finite State Machine

Description

This site contains some supplementary material associated to the detection strategy proposed in [*].

The work [*] proposes an efficient and high-quality strategy to detect stationary foreground objects, which is able to detect not only completely static objects but also partially static ones. Three parallel nonparametric detectors with different absorption rates are used to detect currently moving foreground objects, \mbox{short-term} stationary foreground objects, and long-term stationary foreground objects. The results of the detectors are fed into a novel Finite State Machine that classifies the pixels among background, moving foreground objects, stationary foreground objects, occluded stationary foreground objects, and uncovered background. Results show that the proposed detection strategy is not only able to achieve high quality in several challenging situations but it also improves upon previous strategies.

For any question about the article [*] or about the described test data, please contact Carlos Cuevas at This email address is being protected from spambots. You need JavaScript enabled to view it..

 

Citation

 

[*] C. Cuevas, R. Martínez, D. Berjón, and N. García, "Detection of stationary foreground objects using multiple nonparametric background-foreground models on a Finite State Machine", IEEE Transactions on Image Processing, vol. 26, no. 3, pp. 1127-1142, 2017 (doi: 10.1109/TIP.2016.2642779).

 

Results

 

PETS2006 database:

The original sequences can be downloaded from here.

The ground-truth can be downloaded from here.

Our results can be downloaded from here.

The results obtained with other strategies (without postprocessing stages at region level) can be downloaded from:

LASIESTA database:

The original sequences and the ground-truth can be downloaded from here.

Our results can be downloaded from here.

The results obtained with other strategies (without postprocessing stages at region level) can be downloaded from:

ChangeDetection database:

The original sequences and the ground-truth can be downloaded from here.

Our results can be downloaded from here.

The results obtained with strategy FTSG [4] can be downloaded from here.

i-LIDS database:

The original sequences and the metadata can be downloaded from here.

Our results can be downloaded from here.

 

References

 
1. Y.-L. Tian, M. Lu, and A. Hampapur, “Robust and efficient foreground analysis for real-time video surveillance,” IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1182–1187, 2005.

2. F. Porikli, “Detection of temporarily static regions by processing video at different frame rates,” IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 236–241, 2007.

3. R. H. Evangelio and T. Sikora, “Static object detection based on a dual background model and a finite-state machine,” EURASIP Journal on Image and Video Processing, vol. 2011, no. 1, p. 858502, 2010.

4. R. Wang, F. Bunyak, G. Seetharaman, and K. Palaniappan, “Static and moving object detection using flux tensor with split gaussian models,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 2014, pp. 420–424.