175 research outputs found
Recommended from our members
PETS2009 and Winter-PETS 2009 results: a combined evaluation
This paper presents the results of the crowd image analysis
challenge of the Winter PETS 2009 workshop. The evaluation
is carried out using a selection of the metrics developed
in the Video Analysis and Content Extraction (VACE)
program and the CLassification of Events, Activities, and
Relationships (CLEAR) consortium [13]. The evaluation
highlights the detection and tracking performance of the authors’systems in areas such as precision, accuracy and robustness. The performance is also compared to the PETS
2009 submitted results
Abnormal crowd behavior detection using novel optical flow-based features
In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The
proposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and
direction invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample
deviates significantly from the normal behavior, it is detected as abnormal crowd behavior. Although there are
many optical flow based features for crowd behaviour analysis, this is the first time the angle difference between optical flow vectors in the current frame and in the previous frame is considered as a anomaly feature.
Evaluations on UMN and PETS2009 datasets show that the proposed method performs competitive results compared to the state-of-the-art methods
- …