# Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images
## 5 Discussion
### Comparison with Systems of the State of the Art  
Unfortunately, currently there are not a lot of works where the UTA-RLDD dataset is used, so we are limited when comparing our work to a baseline. The most relevant works at this moment are [22; 23; 36; 37; 38; 39; 40; 41; 42]. In the majority of the works available, the experimentation methodology differs from ours: they extract and classify individual frames from each video and verify whether the classification was correct or not, while we test our systems over the full videos and count the number of alarms raised during each video. Therefore, and although the evaluation of our system is described in terms of videos classified as awake or drowsy, technically the system does not classify the videos, but rather counts the number of alarms raised by the alarm activation module during each video and considers that the system detects the driver as \"awake\" when it does not raise any alarm and \"drowsy\" when it raises at least one. Because of this, some works are not exactly comparable, but it is interesting to check their results nevertheless. It is also important to note that UTA-RLDD provides videos classified in three categories (awake, low vigilant, and drowsy). Some works use all categories, while others perform the classification using only two of the labels (awake and drowsy, as in this work), which is relevant when evaluating results and comparing systems. A summary of the results collected in some of the works that use the UTA-RLDD database is shown in Table 11. As we have mentioned before, we consider that avoiding false positives is critical in this domain, so that drivers do not turn off the alert system. Because of this, besides the global accuracy, we also compare the false positives rate, when possible. It is worth noting that after thoroughly revising the related works, we verified that the false positive rate is only available in Refs. [22] (where three classes are used) and [36; 41; 42] (where two classes are used, but classification is performed using frames instead of videos). In [22], the work where the UTA-RLDD was presented, four methods are used to classify videos as one of the three available categories. These methods obtain global accuracies between 57% and 65%, and the model that obtains the highest accuracy is the HM-LTSM network, with 65.20% accuracy. The accuracy on awake and drowsy videos is high, reaching a notable 80% in both categories. Although these results are very positive, false alarms would be raised in 19% of the cases, so the system could be improved to reduce this rate. It is also interesting to analyze their comparison to a human judgment baseline, in which four volunteers classified the drowsiness level of each video. Human judgment reached a 57.8% accuracy, which is closer to the accuracy obtained by our systems. It makes sense that the fuzzy logic-based system approaches the accuracy obtained by human judgment, since the variables and the rules defined are based on the expert knowledge of humans. The false positive rate is higher, however, while using human judgment, and alerts the driver unnecessarily in 37% of the cases.