GeoComp benchmarks

SZTAKI Surveillance Benchmark Set & ATON Ground Truth Masks for Foreground and Shadow Detection in Video Sequences

This Benchmark set contains raw video frames and binary foreground respectively shadow ground truth masks (drawn by hand), which were used for validation in publications [1] and [2].

From this page, five evaluated sequences can be downloaded: two of them ('Laboratory', 'Highway') come from the 'ATON' benchmark set, but the enclosed ground truth was generated by Cs. Benedek. The three remaining sequences (Sepm, Seam, Senoon) are outdoor surveillance videos which were captured and evaluated by the employees of the MTA-SZTAKI and the Pazmany Peter Catholic University Budapest.

Not all frames of the video sequence have ground truth masks. Corresponding images have the same ordinary number.

Terms of usage:

The benchmark set is free for scientific use.

• Please acknowledge the use of the benchmark by referring to the relevant publications [1] or [2] (as well to 'ATON'[3]).
• Please notify us if a publication using the benchmark set appears

Sample images

SZTAKI Benchmark set:

Seq.	Raw Data	Foreground GT	Shadow GT
SEAM (160 GT frames)
SEPM (75)
SENOON (251)

 

Ground Truth for two videos from the ATON benchmark set

Seq.	Raw Data	Foreground GT	Shadow GT
Laboratory (205)
Highway (170)

 

DOWNLOAD BENCHMARK (75MB)

For password, contact me in e-mail: benedek.csaba_at_sztaki.mta.hu

References:

[1] Cs. Benedek and T. Szirányi: "Bayesian Foreground and Shadow Detection in Uncertain Frame Rate Surveillance Videos", IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 608-621, 2008,

[2] Cs. Benedek and T. Szirányi: "Study on Color Space Selection for Detecting Cast Shadows in Video Surveillance," International Journal of Imaging Systems and Technology, Special Issue on Applied Color Image Processing, vol. 17, no. 3, pp. 190-201, Wiley, 2007

[3] A. Prati, I. Mikiæ, M. Trivedi, R. Cucchiara, "Detecting Moving Shadows: Formulation, Algorithms and Evaluation", IEEE. Trans. Pattern Analysis and Machine Intelligence, vol 25, no 7, July 2003