Displaying 1 to 6 from 6 results

bgslibrary - A background subtraction library

  •    C++

The BGSLibrary was developed early 2012 by Andrews Sobral to provide an easy-to-use C++ framework for foreground-background separation in videos based on OpenCV. The bgslibrary is compatible with OpenCV 2.x and 3.x, and compiles under Windows, Linux, and Mac OS X. Currently the library contains 43 algorithms. The source code is available under GNU GPLv3 license, the library is available free of charge to all users, academic and commercial.

simple_vehicle_counting - Vehicle Detection, Tracking and Counting

  •    C++

Note: the procedure is similar for OpenCV 2.4.x and Visual Studio 2013. Go to Windows console.

imtsl - IMTSL - Incremental and Multi-feature Tensor Subspace Learning

  •    Matlab

Background subtraction (BS) is the art of separating moving objects from their background. The Background Modeling (BM) is one of the main steps of the BS process. Several subspace learning (SL) algorithms based on matrix and tensor tools have been used to perform the BM of the scenes. However, several SL algorithms work on a batch process increasing memory consumption when data size is very large. Moreover, these algorithms are not suitable for streaming data when the full size of the data is unknown. In this work, we propose an incremental tensor subspace learning that uses only a small part of the entire data and updates the low-rank model incrementally when new data arrive. In addition, the multi-feature model allows us to build a robust low-rank background model of the scene. Experimental results shows that the proposed method achieves interesting results for background subtraction task. The source code is available only for academic/research purposes (non-commercial).

ostd - Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences

  •    Matlab

Background subtraction is an important task for visual surveillance systems. However, this task becomes more complex when the data size grows since the real-world scenario requires larger data to be processed in a more efficient way, and in some cases, in a continuous manner. Until now, most of background subtraction algorithms were designed for mono or trichromatic cameras within the visible spectrum or near infrared part. Recent advances in multispectral imaging technologies give the possibility to record multispectral videos for video surveillance applications. Due to the specific nature of these data, many of the bands within multispectral images are often strongly correlated. In addition, processing multispectral images with hundreds of bands can be computationally burdensome. In order to address these major difficulties of multispectral imaging for video surveillance, this paper propose an online stochastic framework for tensor decomposition of multispectral video sequences (OSTD). The source code is available only for academic/research purposes (non-commercial).