Table 1 Comparison of object detection methods in terms of accuracy and computational time

Background subtraction

Mixture of Gaussian model [2–10]

Moderate

Moderate

Simple implementation and good performance but not so well with dynamic background. It requires parameters to be defined by the practitioners. It can capture multi-modal scenarios

Non-parametric background model [12–17]

Moderate to high

Low to moderate

In dynamic background scenarios, NP performs very well compared to MoG-based algorithm. It requires significant post-processing. In occlusion situation, it does not perform well compare to MoG

Temporal differencing [19, 20, 47, 69]

High

Low to moderate

Very good with sudden illumination changes in indoor environment

Warping background [21]

High

Moderate to high

Good in outdoor environment with high background motion. It does not handle occlusion well. Some variations are computationally intensive

Hierarchical background model [22, 66–68]

High

Low to moderate

Make use of both block-based and pixel-based approaches. May be quicker than pixel-based approach, but quality could be compromised

Optical flow [18, 23–26]

Moderate

High

Good with camera motion and crowd detection but highly computation intensive

Spatio-Temporal filter [27–37, 70]

Moderate to high

Low to moderate

Works well for low-resolution scenarios but suffers from noise issues