The exploration of computer vision in interactive artworks is truly fascinating, especially considering its roots in Myron Krueger’s Videoplace from the late 1960s. The idea that the entire human body should be involved in interactions with computers was revolutionary at the time. Videoplace’s ability to digitize a participant’s silhouette and synthesize graphics based on their posture and gestures was ahead of its time. I believe that the notion of involving human body with computer interaction through computer vision to be an essential yet dangerous idea. While it does produce valuable possibilities like the Videoplace or Sorting Daemon, but there could be dangerous implications for our society. For instance, computer vision algorithms may exhibit bias, leading to discriminatory outcomes, especially if the training data is not representative of diverse populations. Biases in such algorithms can result in unfair and discriminatory decisions, affecting individuals in areas like hiring, law enforcement, and financial services. While this can be considered as a interactivity through computer vision, the consequences of this in this contemporary society could affect the lives of many in serious ways.
While reading the computer vision techniques, the notion of semantic understanding piqued my interests. Upon further research I found that unlike text-based data, which inherently carries semantic and symbolic information, digital video, in its raw form, consists of streams of rectangular pixel buffers with no intrinsic meaning therefore making the case for this problem. This lack of inherent semantics hinders a computer’s ability to interpret or extract meaningful information from the video content without additional programming. Bridging the semantic gap seems to be important for developing computer vision algorithms that can discern and interpret the content of video streams, enabling applications to answer even elementary questions about the presence of objects or individuals and the contextual details within a given scene.