GPU-based implementation of an optimized nonparametric background modeling for real-time moving object detection

Answering to the growing demand of computer vision tools for the last generations of consumer electronic devices equipped with smart cameras, several nonparametric moving detection algorithms have been developed. These algorithms, by modeling both background and foreground from spatio-temporal reference data, provide satisfactory results in many complex scenarios. However, to be computationally efficient, they apply some simplifications that decrease the quality of the detections. This paper presents a novel real-time implementation of an optimized spatio-temporal nonparametric moving object detection strategy. To improve the quality of previous algorithms, the bandwidths of the kernels required to model the background are dynamically estimated, and the background model is also selectively updated. The proposed implementation features smart cooperation between a computer/device's Central and Graphics Processing Units (CPU/GPU) and extensive usage of the texture mapping and filtering units of the latter, including a novel method for fast evaluation of Gaussian functions. Thanks to these features, high quality detection rates are achieved while respecting the realtime restrictions imposed by computer vision tools running on current consumer electronic devices.