# Movement-in-a-Video Detection Scheme for Sign Language Gesture Recognition Using Neural Network
## 3 Result and Discussion
Examples of images obtained by applying the sum of frame differences on the sequence of frames from the three datasets are shown in Figure 10. The observations made based on this figure are as follows:  
1. Each video of the three datasets results in a distinct image after the sum-of-the-frame-differences method is applied. Evidently, these images can be used to train an NN to classify the sign language gesture corresponding to the image. 2. As the gesture length increases, the clarity of the paths of hands and arms decreases. That is, whereas the paths of hands and arms can be depicted for short and medium-length gestures, these cannot be easily depicted accurately for long gestures. This is expected because longer gestures have more frames in the sequence. Figure 9: Examples of gestures classified into identical, similar, and unique: (**a**) frame sequence, (**b**) path of the hand, and (**c**) result when the sum of frame differences method is applied. As the gesture length increases, the visibility of the outline of the gesture's body also increases. This is because the longer the gesture is, the higher is the tendency of the gesturer to make small body movements. To demonstrate the effect of the sum of the frame differences method, we illustrated further examples of the result of applying the method on the LSA64 dataset in Figure 8. Figure 10(a) is from videos recorded outdoors (natural lighting), and Figure 10(b) from videos recorded indoors (artificial lighting). The result in Figure 11 clearly shows that the sum-of-the-frame-differences method is successful in detecting the movement in the video regardless of the lighting condition under which the gesture is recorded. Figure 11: Resulting images of the sum of frame differences method applied on LSA64 dataset videos recorded (**a**) outdoors (natural lighting) and (**b**) indoors (artificial lighting). Figure 6:0 Examples of sum of frame differences using (**a**) LSA64, (**b**) DEVISIGN-D, and (**c**) SLR_Dataset. In addition to that, except for the images representing \"red\" and \"green\", all the resulting images can be easily distinguished. This enables the mapping of the resulting image to its corresponding gesture without the need for the information in the corresponding video. Meanwhile, a closer examination of the images representing \"red\" and \"green\" is necessary to identify the corresponding gesture. This is because the two hand gestures differ only in the hand pose. Even when actually performed, it is difficult to identify the gesture unless we observe the hand pose. This is expected because the gestures are classified as identical gestures. To observe the result of applying the proposed method on (1) identical, (2) similar, and (3) unique gestures, examples are given in Figure 9c. The images resulting from the application of the sum of the frame differences method to the sequence of frames in Figure 9a are shown in Figure 9c. The following observations are made:  
1. The images corresponding to the sum of frame differences of identical gestures are also identical. This is expected because the gestures differ only in hand pose. 2. The resulting images from the sum of frame differences of similar gestures are distinguishable. This is expected because notwithstanding the similarity in the path of the hand while the gesture is performed, the hand pose and direction are different. 3. Similar to the results for identical gestures, the images resulting from the sum of frame differences for the unique gesture is distinguishable. This is expected because the trajectory and hand pose while performing the gesture is different from those of the remaining gestures. Previous observations are focused on comparing the images resulting from the frame differences for three gesture categories. The following observations are focused on the images resulting from the sum of frame differences in general:  
1. The gesture must be performed with a still background.