Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138402
Title: Recognising activities using motion history
Authors: Xu, Wilson Weixuan
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2020
Publisher: Nanyang Technological University
Project: A1048-191
Abstract: Human Activity Recognition (HAR) is a daunting task of computer vision. Complex algorithms are required to recognize actions performed, through spatial-temporal information obtained from video sequences. Large computing power is required to process this information. Motion History Images (MHI) can represent this information in a single image, hence it can be used to reduce the complexities and hardware demands in implementing HAR. The objective of this project was to implement HAR using MHI. The implementation first involved the acquisition of video sequences. Frames from the video sequences were then pre-processed and converted into MHI for annotation and creation of dataset. A convolutional neural network (CNN) model was used to train on the dataset. The model was then validated and tested to evaluate its effectiveness, before being integrated into the HAR program. While the model performed very well for the validation set, there were mixed results for the testing set. The poorer results were due to insufficient intraclass variation in some classes in the training set, and the model responded not as well to actions that were slightly different. However, the better results demonstrate that certain actions can be recognized well in a generalized setting. In integrating the HAR program, results showed that it is unable to run in real-time due to hardware constraints, but real-time speeds are attainable, through using better computing hardware. Future works on this project include varying the conditions used for the recording of actions performed to enable the model to generalize better in HAR. The use of better computing hardware will enable the HAR program to run in real-time, and in turn deployable in real life applications.
URI: https://hdl.handle.net/10356/138402
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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