# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 2 Related Work
### Object Detection
#### 2.1.3 Background Substraction  
As depicted by [PERSON] at al. in [9], one of the key techniques for automatic video analysis is background subtraction, especially in the domain of video surveillance. The goal is to distinguish between foreground and background areas in video sequences using a background model.  
The main task of a background subtraction method is to compare an input frame against a background model [10]. This model describes the static background area of the image. Depending on the used methodology the model consists of different kind of features. After establishing a background model each input frame is subtracted by the static background model. This subtraction is also referred to as foreground detection. Basically it is a determination of areas belonging to the foreground class resulting in a binary foreground mask. However, in order to cope with several challenges regarding real world scenarios, post-processing steps such as filtering or refining the foreground mask needs to be performed. Based on the system requirements all other processing steps, such as labelling or counting detected regions, can be built upon the background subtraction task. Figure 1 illustrates the typical background subtraction process, i.e., for a video surveillance task.  
Due to the nature of video sequences background subtraction algorithms have to cope with several challenges. Some issues depicted by [PERSON] [9] include gradual/sudden illumination changes, dynamic background, camouflage, shadows and video noise. To overcome challenges as mentioned before several background models have evolved. These models are explained in more detail in the subsequent sections:  
* _Static Background image_ According to [PERSON] [6], one of the simplest approaches to the background model is to pick a static frame of the video and hope that there are no moving objects inside. In the video surveillance field it is often a common technique due to the fact that these videos start with a completelystatic scene. Nevertheless, the drawback of this method is that it cannot deal with illumination changes. Thus, even minor changes in camera or object position problems as well as the addiction or removal of objects from the static scene.
* _Frame Difference_ Frame differencing is another straightforward method that subtracts one frame from another frame as depicted by [PERSON] et al. in [11]. Furthermore, it is also possible to subtract from several frames later. In [6], this method eliminates the issue of needing a (static) background image to be updated. The difference for moving objects is dependent on the amount of moving and the appearance of objects in the scene. Nonetheless, it is quite difficult to analyse such image differences.
* _Running Average_ Conforming to [11], this attempt is used to incorporate background changes to the model. Based on the algorithm the running average method uses a certain number of last frames to generate an update for the background model accordingly. To control the amount of change with which the background model gets updated, there is a learning rate which has to be set.  
Conforming to [PERSON] [6], the Gaussian Mixture Model is considered to be the standard method for modelling the background although there are newer and more complex methods in existence. It is often used as a benchmark for other background modelling techniques.