The detection of moving objects has become one of the most focused and challenging areas from the last few decades in the domain of computer vision. Background subtraction (BGS) is one of the most widely used and effective techniques for motion-based detection of objects in incessant video sequences from static or moving cameras in real-time applications. It evaluates the current frame by subtracting it from the background model frame. This chapter explores various challenging issues that occur during detection of moving objects, steps of the basic BGS technique for detecting the motion-based object from a video sequence, resources, and data sets. This chapter also provides a comparative study of several existing methods in terms of both quality and quantitative analysis.