Abstract
Acknowledging the advantages as well as the dangers of the internet content on kids education and entertainment, YouTube Kids was created. Based on regulations for child-friendly programs, several violations are identified and restricted from viewable content. When a child surfs the Internet, the same regulations could be automatically detected and filtered. However, current YouTube Kids content filtering relies on meta-data attributes, where inappropriate content could pass the filtering mechanism. This research, propose an advanced real-time content filtering approach using automated video and audio analysis as an extra layer for kids safety. The proposed method utilizes the thin-slicing theory, where several one second slices are selected randomly from the clip and extracted. The use of a one-second slice will assure a temporal analysis of the clip content, and ensures a real-time content analysis. For each slice, audio is automatically transcribed using automatic speech recognition techniques to be further analysed for its linguistic content. Furthermore, the audio signal is analysed to detect event and scenes (e.g. explosion). The image frames extracted from the slices are also inspected for its content to avoid inappropriate scenes, such as violence. Upon the success of this approach on YouTube Kids application, investigation of its generalizability to other video applications, and other languages could be performed.