A hybrid adaptive scheme based on selective Gaussian modeling for real-time object detection

Object detection is receiving a growing attention with the emergence of surveillance systems. This paper presents a hybrid adaptive scheme based on selective Gaussian modeling for detecting objects in complex outdoor scenes with gradual illumination changes and dense, moving background objects like swinging tree branches. The proposed technique combines simple frame difference (FD), simple adaptive background subtraction (BS), and accurate Gaussian modeling to benefit from the high detection accuracy of Mixture of Gaussian solution (MoG) in outdoor scenes while reducing the computations required, thus, making it faster and more suitable for real time surveillance applications. Moreover, by applying selective component matching and updating and hysteresis thresholding, the probability of detecting a background pixel as foreground decreases leading to better detection accuracy than MoG as demonstrated in the quantitative and qualitative comparison.