Automated daily human activity recognition for video surveillance using neural network

Mohanad Babiker , Othman O. Khalifa, Kyaw Kyaw Htike, Aisha Hassan and Muhamed Zaharadeen, “Automated daily human activity recognition for video surveillance using neural network,” 2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), Putrajaya, 2017, pp. 1-5.

doi: 10.1109/ICSIMA.2017.8312024


Surveillance video systems are gaining increasing attention in the field of computer vision due to its demands of users for the seek of security. It is promising to observe the human movement and predict such kind of sense of movements. The need arises to develop a surveillance system that capable to overcome the shortcoming of depending on the human resource to stay monitoring, observing the normal and suspect event all the time without any absent mind and to facilitate the control of huge surveillance system network. In this paper, an intelligent human activity system recognition is developed. Series of digital image processing techniques were used in each stage of the proposed system, such as background subtraction, binarization, and morphological operation. A robust neural network was built based on the human activities features database, which was extracted from the frame sequences. Multi-layer feed forward perceptron network used to classify the activities model in the dataset. The classification results show a high performance in all of the stages of training, testing and validation. Finally, these results lead to achieving a promising performance in the activity recognition rate.

keywords: {feature extraction;feedforward neural nets;image sequences;learning (artificial intelligence);multilayer perceptrons;video signal processing;video surveillance;activities model;activity recognition rate;automated daily human activity recognition;computer vision;digital image processing techniques;human activities features database;human movement;human resource;intelligent human activity system recognition;normal event;robust neural network;surveillance video systems;suspect event;video surveillance;Activity recognition;Feature extraction;Legged locomotion;Mathematical model;Training;Video surveillance;Human activities recognition;Multi-layer feed forward perceptron;Neural Network;intelligent system;video surveillance},