Human Action Recognition on Real Time and Offline Data

Abstract

Human action recognition is an important area of research in the field of computer vision due to its extensive applications like security surveillance; content based video retrieval and annotation, human computer interaction, human fall detection, video summarization, robotics, etc. The surveillance system deals with the monitoring and analysing the human behaviour and activities. The main aim of the smart surveillance system is to recognize anomalous behaviour in given scene and provide real time intimation to relevant person. We have designed and tested Smart Surveillance System for College Corridor Scene (3S2CS). The system recognises the anomalous behaviour and an intimation is provided in the form of Firebase Cloud Messaging (FCM) alert on the android mobile phone to the authorised user. This paper mainly discuss the methodologies used for the human action recognition. The basic step is to provide video as an input. These videos are further divided into number of frames. The videos are used for training and for each video, Scale Invariant Feature Transform (SIFT) is applied for extracting features and developing feature vectors. The actions are classified using K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifier. Two standard offline datasets considered for testing are Weizmann and UTD-MHAD. For real time scenario we have created dataset in college campus called as College Corridor dataset. It contains student activities like falling, fighting, walking, running, sitting and other general actions. If falling or fighting action is detected, the notification is sent to the authorized user who has installed ActionDetector android application and registered a device for the same. Action recognition accuracy is 92.91% using SVM and 90.83% using KNN.