Abnormal Action Recognition in Crowd Scenes via Deep Data Mining and Random Forest

Human activities that deviate from the norm are deemed abnormal, and such individuals are referred to as anomalous objects. Employing visual data to detect abnormal behaviour is a complex topic in video processing. This research proposes a novel method for detecting abnormal behaviour in complicated, crowded environments. In this article, we proposed a robust method for abnormal action recognition. We initially processed the data, applying fuzzy c mean and super pixel-based segmentation, extracting the features and tracking the object. The next step is to optimize the data. We used a deep data mining approach via t-distributed stochastic neighbor embedding procedure, and for classification, we applied random forest. We achieved 80.24% accuracy rate for human detection over UCSD dataset, and 79.19% for Shanghai tech dataset. We also got 84.00% accuracy of abnormal action recognition over UCSD dataset and 82.00% over Shanghai tech dataset.