Systems and methods for detecting anomalies in videos

Abstract

Systems and methods are provided for detecting anomalies in videos (e.g., surveillance videos) in a distributed manner to enhance data privacy and improve model robustness, model accuracy, and scalability of the system. Computer-based systems and methods can ensure enhanced privacy among different computing devices/clients while training a machine learning model without the requirement of data sharing with a server. A data trimming method, a histogram-based noise cleansing method, a frame selection method, a data distribution method among different clients, and a convolutional neural network and recurrent neural network model can be employed for video classification.