Abstract
In this work we apply the MIL ranking loss proposed in Sultani et al. (2018) in a different way than the original work. Videos are disposed into clips with fixed length, rather than a fixed number of segments. Furthermore, feature vectors are obtained by concatenating feature vectors from two adjacent segments. We argue that this procedure makes it easier for the algorithm to distinguish between normal and anomalous segment scores. Since with fixed-length clips the number of segments varies within videos, we balance the video mini-batch during the training phase with the same number of segments, for the smallest video in the mini-batch. Experiments with Shanghaitech dataset show that this procedure is able to improve performance when compared against the original proposal.
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Pereira, S.S.L., Maia, J.E.B. (2023). Improving MIL Video Anomaly Detection Concatenating Deep Features of Video Clips. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-27440-4_36
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