Study on Anomaly Detection in Crowd Scene
Jun Zhang, Yunxia Chu
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.122How to use a DOI?
- Crowd Scene, Anomaly detection, Bag-of-words, Probabilistic Latent Semantic Analysis(PLSA), Interest Points
- Anomaly detection technology in crowd scene is very important in public place. Crowd detection differs from pedestrian detection which we assume no individual pedestrian can be properly segmented in the image. We propose a scheme which the scen can be treated the crowd motion patterns as the spatial-temporal domain. In the classification stage, we divide whole frame into small blocks, and motion pattern in each block is encoded by the distribution of motion bags in it. PLSA classifier is proposed to infer classification of crowed detection, and we classify motion pattern into normal or abnormal group according to the deviation between motion pattern and train model. The comprehensive implementation can detect crowd in real-time. This paper presents an approach to automatically detect abnormal behavior in crowd scene with Interest points to represent moving objects to generate word of bags, which are used to describe crowed moriment results show that the speed of detection has been greatly improved using our approach.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jun Zhang AU - Yunxia Chu PY - 2015/12 DA - 2015/12 TI - Study on Anomaly Detection in Crowd Scene BT - 2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering PB - Atlantis Press SP - 604 EP - 609 SN - 2352-538X UR - https://doi.org/10.2991/icmmcce-15.2015.122 DO - https://doi.org/10.2991/icmmcce-15.2015.122 ID - Zhang2015/12 ER -