A Proposed Data Stream Clustering Method for Detecting Anomaly Events in Crowd Scene Surveillance videos | ||
| Diyala Journal For Pure Science | ||
| Article 1, Volume 13, Issue 4, December 2017, Pages 126-136 | ||
| Authors | ||
| Abdulamir A. Karim; Narjis Mezaal Shati | ||
| Abstract | ||
| In this research, a new data stream clustering method utilizing seed based region growing technique is implemented to perform abnormal event detection in anomaly detection system in a new data stream clustering method used in abnormal detection system. This is done by applying HARRIS or FAST detectors on the frames of video clips in two publically available datasets. The first UCSD pedestrian dataset (ped1 and ped2 datasets), and the second VIRAT video dataset system to extract list of pairs of interest points. From these pairs a list of features such as: distance, direction, x-coordinate, y-coordinate obtained to use as an input to the new clustering method. This method in using HARRIS detector achieves detection rates about (9.09%, 52.17%, 61.67%), and the false alarm rates are (18.79%, 36.09%, 66.67%) by using Ped1, Ped2, and VIRAT datasets respectively. For the case of using FAST detector, the best- achieved detection rates are (7.88%, 46.09%, 58.33%), and the false alarms are (21.21%, 40.87%, 63.33%) by using the three previously mentioned benchmarks respectively. | ||
| Keywords | ||
| Surveillance Systems; Anomaly Detection; Crowed Scene Detection; Anomaly Events; Abnormal Event Detection | ||
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