In the previous years, a number of studies have been conducted on analyzing human action through videos which are recorded with plain backgrounds or with cluttered background. Furthermore, analyzing human action can create a way to detect malicious acts like vandalism which is common in university grounds like in University of the Philippines Los Baños. Consequently, detecting vandalism can be a medium for more advance technology. An instance is when the detection of vandalism is linked to a surveillance device, an indication like an alarm can be made when an act of vandalism is detected. This can lead to the instantaneous arrest of the doer. (Importance of the subject matter; it is stated here that previous studies about the subject matter have been conducted whether in clean backgrounds or in cluttered
They evaluated this algorithm on human action detection using a public video with cluttered background and detection of customers showing an intention of purchasing the items on the shelf in a shopping mall. (A study on human action analysis that also supports the summative introduction; a study that proposes and eventually led to the creation of the of an algorithm for detecting human actions called SMILE-SVM) Moreover, in 2009 Ming-Yu Chen and Alexander Hauptmann had a study that led to the foundation of MoSIFT, an algorithm that detects interest points to analyze human motion. It is based on the SIFT, a well-known descriptor. It resulted on a 95.8% accuracy on KTH dataset, a database of human actions like walking, jogging, running and more. (A study that also support the summative introduction; a study that also presented another algorithm for detection of human