# A Comprehensive Review of Computer Vision in Sports:
## 3 Play Field Extraction in Various Sports  
Detection of the sports field plays an important role in sports video analysis. Detection of the playfield region has two objectives. One is to detect the playfield region from non-playfield areas, while the other is to identify primary objects from the background by filtering out redundant pixels such as grass and court lines. This provides a reduced pixel which requires processing and reduction in errors to simplify player or ball detection and tracking phases, event extraction, pose detection, etc. The challenges here include distinguishing the color of the playfield from that of the stadium, lighting conditions and sometimes weather, viewing angles, and the shadows. Therefore, accurate segmentation of the playfield cannot be achieved just by processing the color of the playfield under certain situations and making it constant without updating the statistics throughout the game. There is also an added noise when the player's clothes matching that of the ground, and there appear shadows at the base of a player from different sources of light. A Gaussian-based background subtraction technique [14], which is implemented using computer vision methods, generates the foreground mask as shown in Figure 5.  
Figure 4: Sports wise research progress. Figure 5: Background subtraction model. Researchers have used a single dominant color for detecting the playfield. Accordingly, some studies have utilized the features of images in which illumination is not affected by transforming the images from RGB space to HIS [15; 16; 17], YCbCr [18], normalized RGB [19; 20; 21]. For a precise capture of the movements of the players, tracking the ball and actions of referees on the field or court, it is necessary to calibrate the camera [5; 8] and also to use an appropriate number of cameras to cover the field. Though some algorithms are capable of tracking the players, some other objects also need to be tracked in dynamically complex situations of interest for detailed analysis of the events and extraction of the data of the subject of interest. Reference [22] presented an approach to extract the playing field and track the players and ball using multiple cameras in soccer video. In [23; 24], an architecture was presented, which uses single (Figure 6a) and multi-cameras (Figure 6c) to capture a clear view of players and ball in various challenging and tricky situations such as severe occlusions and the ball being missing from the frames. To estimate the players' trajectory and team classification in [25; 26] a bird's eye view of the field is presented to capture players, precisely as shown in Figure 6b. Various positions of the camera for capturing the entire field are presented in [27; 28] to detect and track the players/ball and estimate the position of the players. Figure 6: _Cont._  
Morphological operations-based techniques can separate the playfield and non-playfield regions, but they cannot detect the lines in the playfield. The background subtraction-based techniques generate foreground regions by subtracting the background frame from the current frame (i.e., by detecting moving objects in the frame); however, they fail to detect the playfield lines as shown in Figure 5. So, the best way to detect the playfield lines is by labeling the data as playfield lines (as shown in Figure 7a), advertisements (as shown in Figure 7b), and the non-playfield regions as shown in Figure 7c. Training the model using a dataset that is labeled as playfield lines, advertisements, and the non-playfield region as shown in Figure 7 can detect and classify the playfield lines, advertisements, and the non-playfield region, which reduces the detection of false positives and false negatives. Figure 6: Camera placements in the playfield. (**a**) Ceiling-mounted camera [23]. (**b**) Birds eye view of the field [25,26]. (**c**) Multiple cameras placed to cover the complete playfield [27].