Research over detecting anomalous human behavior in crowded scenes has created much attention due to its direct applicability over a large number of real-world security applications. In this work, we propose a novel statistical feature descriptor to detect violent human activities in real-world surveillance videos. Standard spatiotemporal feature descriptors are used to extract motion cues from videos. Finally, a discriminative SVM classifier is used to classify violent/non-violent scenes present in the videos with the help of feature representation formed out of the proposed statistical descriptor. Efficiency of the proposed approach is tested on crowd violence and hockey fight benchmark datasets.
Violence detection Statistical features Histogram of gradients SVM (support vector machines)
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Xian, Y., et al.: Evaluation of low-level features for real-world surveillance event detection. IEEE Trans. Circuits Syst. Video Technol. 27(3), 624–634 (2017)CrossRefGoogle Scholar
Barrett, D.P., Siskind, J.M.: Action recognition by time series of retinotopic appearance and motion features. IEEE Trans. Circuits Syst. Video Technol. 26(12), 2250–2263 (2015)CrossRefGoogle Scholar
Rodriguez, M., et al.: One-shot learning of human activity with an MAP adapted GMM and simplex-HMM. IEEE Trans. Cybern. 47(7), 1769–1780 (2017)CrossRefGoogle Scholar
Zhang, T., et al.: Discriminative dictionary learning with motion weber local descriptor for violence detection. IEEE Trans. Circuits Syst. Video Technol. 27(3), 696–709 (2017)CrossRefGoogle Scholar
Wang, S., et al.: Anomaly detection in crowded scenes by SL-HOF descriptor and foreground classification. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE (2016)Google Scholar
Uijlings, J., et al.: Video classification with densely extracted hog/hof/mbh features: an evaluation of the accuracy/computational efficiency trade-off. Int. J. Multimed. Inf. Retr. 4(1), 33–44 (2015)CrossRefGoogle Scholar