Abstract
People are always trouble with drowning problems and want a drowning detection method. Almost existing methods extract simulated drowning features for supervised classification, but drowning events are rare abnormal events that are difficult to really simulate. In this paper, an unsupervised video anomaly detection method is proposed to detect pool drowning events. At first, we make a new dataset of pool scenes. Drowning events are only in the test set and the train set only includes pool normal events. Pool dataset is preprocessed and a neural network modified from ResNet is proposed to reconstruct input video frames. The differences between reconstructed frames and ground truth frames are compared to detect anomalous events not in the training set. Experiments show that proposed method is more applicable to video anomaly detection in pool scenes than existing methods and it is feasible that methods based on video anomaly detection for drowning detection.
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Acknowledgment
The authors would like to thank the National Natural Science Foundation of China (61771412, 61871336 and 62071401).
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He, X., Yuan, F., Zhu, Y. (2021). Drowning Detection Based on Video Anomaly Detection. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_57
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DOI: https://doi.org/10.1007/978-3-030-87361-5_57
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