Abstract
This Chapter presents a framework for the the task of abnormality detection in crowded scenes based on the analysis of trajectories, build up upon a novel video descriptor, called Histogram of Oriented Tracklets. Unlike standard approaches that employ low level motion features, e.g. optical flow, to form video descriptors, we propose to exploit mid-level features extracted from long-range motion trajectories called tracklets, which have been successfully applied for action modeling and video analysis. Following standard procedure, a video sequence is divided into spatio-temporal cuboids within which we collect statistics of the tracklets passing through them. Specifically, tracklets orientation and magnitude are quantized in a two-dimensional histogram which encodes the actual motion patterns in each cuboid. These histograms are then fed into machine learning models (e.g., Latent Dirichlet allocation and Support Vector Machines) to detect abnormal behaviors in video sequences. The evaluation of the proposed descriptor on different datasets, namely UCSD, BEHAVE, UMN and Violence in Crowds, yields compelling results in abnormality detection, by setting new state-of-the-art and outperforming former descriptors based on the optical flow, dense trajectories and social force models.
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Notes
- 1.
Available at http://www.ces.clemson.edu/~stb/klt/.
- 2.
Available at http://www.svcl.ucsd.edu/projects/anomaly/.
- 3.
- 4.
- 5.
Available at http://www.openu.ac.il/home/hassner/data/violentflows/.
- 6.
The Equal Error Rate is the value of false positive rate when the ROC curve intersects the line connecting (0, 1)–(1, 0).
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Mousavi, H., Galoogahi, H.K., Perina, A., Murino, V. (2016). Detecting Abnormal Behavioral Patterns in Crowd Scenarios. In: Esposito, A., Jain, L. (eds) Toward Robotic Socially Believable Behaving Systems - Volume II . Intelligent Systems Reference Library, vol 106. Springer, Cham. https://doi.org/10.1007/978-3-319-31053-4_11
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