Abstract
The automatic detection of suspicious abandoned objects has become a priority in video surveillance in the last years. Terrorist attacks, improperly parked vehicles, abandoned drug packages and many other events, endorse the interest in automating this task. It is challenge to detect such objects due to many issues present in public spaces for video-sequence process, like occlusions, illumination changes, crowded environments, etc. On the other hand, using deep learning can be difficult due to the fact that it is more successful in perceptual tasks and generally what are called system 1 tasks. In this work we propose to use topological features to describe the scenery objects. These features have been used in objects with dynamic shape and maintain the stability under perturbations. The objects (foreground) are the result of to apply a background subtraction algorithm. We propose the concept the surveillance points: set of points uniformly distributed on scene. Then we keep track of the changes in a cubic region centered at each surveillance points. For that, we construct a simplicial complex (topological space) from the k foreground frames. We obtain the topological features (using persistent homology) in the sub-complexes for each cubical-regions, which represents the activity around the surveillance points. Finally for each surveillance points we keep track of the changes of its associated topological signature in time, in order to detect the abandoned objects. The accuracy of our method is tested on PETS2006 database with promising results.
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Notes
- 1.
We obtained good results when the abandoned objects have dimensions not greater than \((w_T/2n) \times (h_T/2n)\).
- 2.
- 3.
According expert criterion for application area (airport, train station, etc) and empirical study from data sets.
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Lamar Leon, J., Alonso Baryolo, R., Garcia Reyes, E., Gonzalez Diaz, R., Salgueiro, P. (2024). Abandoned Object Detection Using Persistent Homology. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469. Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_13
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