Optimizing Neural Network to Develop Loitering Detection Scheme for Intelligent Video Surveillance Systems

Yonghwa Kim, Yoo-Sung Kim


Using an optimized neural network as machine learning classification model is proposed to develop an enhanced loitering detection scheme for intelligent video surveillance systems. From input surveillance videos, the moving pattern features including the looking-around feature are extracted from the partial trajectories of each pedestrian. For training loitering detection scheme with the extracted features, we tried to optimize the structure and the training parameters of neural network with respect to the detection accuracy and the model building time. And as the experiments, the detection accuracy of the optimized neural network is compared with those of other machine learning classification models such as decision tree, naïveBayesian, and support vector machine. From the experiment results, the loitering detection scheme using the optimized neural network can achieve better detection accuracy of 93.33% than using other models.


Mathematics of Computing/optimization, Computing Methodologies/Artificial Intelligence


  • There are currently no refbacks.

Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.