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2019 | OriginalPaper | Buchkapitel

A Machine Learning Approach to Detect Violent Behaviour from Video

verfasst von : David Nova, André Ferreira, Paulo Cortez

Erschienen in: Intelligent Technologies for Interactive Entertainment

Verlag: Springer International Publishing

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Abstract

The automatic classification of violent actions performed by two or more persons is an important task for both societal and scientific purposes. In this paper, we propose a machine learning approach, based a Support Vector Machine (SVM), to detect if a human action, captured on a video, is or not violent. Using a pose estimation algorithm, we focus mostly on feature engineering, to generate the SVM inputs. In particular, we hand-engineered a set of input features based on keypoints (angles, velocity and contact detection) and used them, under distinct combinations, to study their effect on violent behavior recognition from video. Overall, an excellent classification was achieved by the best performing SVM model, which used keypoints, angles and contact features computed over a 60 frame image input range.

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Afsar, P., Cortez, P., Santos, H.: Automatic visual detection of human behavior: a review from 2000 to 2014. Expert Syst. Appl. 42(20), 6935–6956 (2015). https://doi.org/10.1016/j.eswa.2015.05.023CrossRef

Afsar, P., Cortez, P., Santos, H.M.D.: Automatic human trajectory destination prediction from video. Expert Syst. Appl. 110, 41–51 (2018). https://doi.org/10.1016/j.eswa.2018.03.035CrossRef

Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR (2017)

Chéron, G., Laptev, I., Schmid, C.: P-CNN: pose-based CNN features for action recognition. CoRR abs/1506.03607 (2015). http://arxiv.org/abs/1506.03607

Clarin, C.T., Dionisio, J.A.M., Echavez, M.T., Naval, P.C.: DOVE: detection of movie violence using motion intensity analysis on skin and blood. Technical report, University of the Philippines (2005)

Coppola, C., Faria, D., Nunes, U., Bellotto, N.: Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5055–5061 (2016)

Datta, A., Shah, M., Lobo, N.D.V.: Person-on-person violence detection in video data. In: Object Recognition Supported by User Interaction for Service Robots, vol. 1, pp. 433–438, August 2002. https://doi.org/10.1109/ICPR.2002.1044748

Deniz, O., Serrano, I., Bueno, G., Kim, T.: Fast violence detection in video. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol. 2, pp. 478–485, January 2014

Dong, Z., Qin, J., Wang, Y.: Multi-stream deep networks for person to person violence detection in videos. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds.) Pattern Recognition, pp. 517–531. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-3002-4_43CrossRef

10.

Du, W., Wang, Y., Qiao, Y.: RPAN: an end-to-end recurrent pose-attention network for action recognition in videos. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3745–3754, October 2017. https://doi.org/10.1109/ICCV.2017.402

11.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385

12.

Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. CoRR abs/1404.7584 (2014). http://arxiv.org/abs/1404.7584

13.

Herath, S., Harandi, M.T., Porikli, F.: Going deeper into action recognition: a survey. CoRR abs/1605.04988 (2016). http://arxiv.org/abs/1605.04988

14.

Kong, Y., Fu, Y.: Human Action Recognition and Prediction: A Survey. ArXiv e-prints, June 2018

15.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012). http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

16.

Li, L., Zheng, W., Zhang, Z., Huang, Y., Wang, L.: Skeleton-based relational modeling for action recognition. CoRR abs/1805.02556 (2018). http://arxiv.org/abs/1805.02556

17.

Liu, J., Shahroudy, A., Xu, D., Wang, G.: Spatio-temporal LSTM with trust gates for 3D human action recognition. CoRR abs/1607.07043 (2016). http://arxiv.org/abs/1607.07043

18.

Luvizon, D.C., Picard, D., Tabia, H.: 2D/3D pose estimation and action recognition using multitask deep learning. CoRR abs/1802.09232 (2018). http://arxiv.org/abs/1802.09232

19.

Ng, A.: Machine Learning Yearning. deeplearning.ai (2018)

20.

Pham, H., Khoudour, L., Crouzil, A., Zegers, P., Velastin, S.A.: Exploiting deep residual networks for human action recognition from skeletal data. CoRR abs/1803.07781 (2018). http://arxiv.org/abs/1803.07781

21.

Pham, H., Khoudour, L., Crouzil, A., Zegers, P., Velastin, S.A.: Learning and recognizing human action from skeleton movement with deep residual neural networks. CoRR abs/1803.07780 (2018). http://arxiv.org/abs/1803.07780

22.

Sudhakaran, S., Lanz, O.: Learning to detect violent videos using convolutional long short-term memory. CoRR abs/1709.06531 (2017). http://arxiv.org/abs/1709.06531

23.

Vasconcelos, N., Lippman, A.: Towards semantically meaningful feature spaces for the characterization of video content. In: Proceedings of International Conference on Image Processing, vol. 1, pp. 25–28, October 1997. https://doi.org/10.1109/ICIP.1997.647375

24.

Wang, Q.: A survey of visual analysis of human motion and its applications. CoRR abs/1608.00700 (2016). http://arxiv.org/abs/1608.00700

25.

Witten, I., Frank, E., Hall, M., Pal, C.: Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann, San Franscico (2017)

26.

Zolfaghari, M., Oliveira, G.L., Sedaghat, N., Brox, T.: Chained multi-stream networks exploiting pose, motion, and appearance for action classification and detection. CoRR abs/1704.00616 (2017). http://arxiv.org/abs/1704.00616

Titel: A Machine Learning Approach to Detect Violent Behaviour from Video
verfasst von: David Nova
André Ferreira
Paulo Cortez
Verlag: Springer International Publishing
Buch: Intelligent Technologies for Interactive Entertainment
Print ISBN: 978-3-030-16446-1

Electronic ISBN: 978-3-030-16447-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-16447-8_9

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