NonparametricTrackingGPU

 

Research  

 

GTI Data   

 

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

 

Databases  


LaSoDa (2021): 60 annotated images from matches in five stadiums with different characteristics and light conditions.

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.

PIROPO Database (2016): People in Indoor ROoms with Perspective and Omnidirectional cameras.

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  


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  


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)

 

                                                                                                                                                                                                                             
 

                                                                   
 
                                                                                                                                                             
 

      

 

 

Real-Time nonparametric background subtraction with tracking-based foreground update

Description


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

  • Downloadable software pack.
  • Results.

The work [*] proposes a high-quality nonparametric moving object detection strategy, along with its real-time implementation in a GPU. This strategy features robust spatiotemporal models of both the background and the foreground, the latter augmented with a novel tracking system based on a particle filter capable of dealing with a variable and unknown number of moving regions.The filter updates the positions of reference data and provides prior probability estimations for a Bayesian classifier that is able to combine spatio-temporal models with different spatial distribution of reference data. In addition, the background model is selectively analyzed thanks to the automatic selection of regions of interest in the input images, yielding equivalent results at a fraction of the computational cost.

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

Citation


[*] D. Berjón, C. Cuevas, F. Morán, and N. García, "Real-time nonparametric background subtraction with tracking-based foreground update", Pattern Recognition, vol. 74, pp. 156-170, Feb. 2018. (doi: 10.1016/j.patcog.2017.09.009).

Software


The source code can be downloaded here and a binary can be downloaded here.

The binary has been compiled for 64-bit Linux systems and a GPU with CUDA capabilities >= 2.0; it depends on the following external libraries to be installed:

  • CUDA Runtime, version >= 4.x
  • FreeImagePlus, version >= 3.x
  • Boost.Filesystem and Boost.System, version >= 1.54
  • Qt, version >= 5.x

Only two parameters may be indicated in the execution of the binary:

  • Number of reference images in the background modeling.
    • It must be high enough to cover the dynamic changes of the background.
    • Default value: 200
  • Width (appearance) of the Gaussian kernels in the foreground modeling.
    • The larger this value, the less selective is the foreground model, and therefore it has less weight in the Bayesian classifier.
    • Default value: 0.02 (range [0,1]).

Results:

SABS database [1]:

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

Our results can be downloaded from here.

STAR database [2]:

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

Our results can be downloaded from here.

LASIESTA database:

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

Our results can be downloaded from here.

Software


1. S. Brutzer, B. Höferlin, and G. Heidemann, “Evaluation of background subtraction techniques for video surveillance,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1937–1944, 2011.
2. L. Li, W. Huang, I. Y.-H. Gu, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” IEEE Transactions on Image Processing, vol. 13, no. 11, pp. 1459–1472, 2004.