Singh, K., Rajora, S., Vishwakarma, D. K., Tripathi, G., Kumar, S., & Walia, G. S. (2020). Crowd anomaly detection using aggregation of ensembles of fine-tuned convnets. Neurocomputing, **371**, 188-198.

Siva, P., & Xiang, T. (2010). Action detection in crowd. In *BMVC* (pp. 1-11).

Solera, F., Calderara, S., & Cucchiara, R. (2016). Socially constrained structural learning for groups detection in crowd. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, **38**(5), 995-1008.

Solmaz, B., Moore, B. E., & Shah, M. (2012). Identifying behaviors in crowd scenes using stability analysis for dynamical systems. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, **34**(10), 2064-2070.

Song, X., Zhao, H., Cui, J., Shao, X., Shibasaki, R., & Zha, H. (2013). An online system for multiple interacting targets tracking: Fusion of laser and vision, tracking and learning. *ACM Transactions on Intelligent Systems and Technology (TIST)*, **4**(1), 18.

Soomro, K., Zamir, A. R., & Shah, M. (2012). *UCF101: A dataset of 101 human actions classes from videos in the wild*. arXiv:1212.0402.

Spampinato, C., Palazzo, S., & Giordano, D. (2012). Evaluation of tracking algorithm performance without ground-truth data. In *2012 19th IEEE international conference on image processing* (pp. 1345-1348).

Sugimura, D., Kitani, K. M., Okabe, T., Sato, Y., & Sugimoto, A. (2009). Using individuality to track individuals: Clustering individual trajectories in crowds using local appearance and frequency trait. In *2009 IEEE 12th international conference on computer vision* (pp. 1467-1474). IEEE.

Sultani, W., Chen, C., & Shah, M. (2018). Real-world anomaly detection in surveillance videos. In *Proceedings of the IEEE conference on computer vision and pattern recognition* (pp. 6479-6488).

Sun, L., Jia, K., Chan, T., Fang, Y., Wang, G., & Yan, S. (2014). DL-SFA: Deeply-learned slow feature analysis for action recognition. In *2014 IEEE conference on computer vision and pattern recognition* (pp. 2625-2632).

Tang, Y., Ma, L., Liu, W., & Zheng, W. (2018). Long-term human motion prediction by modeling motion context and enhancing motion dynamic. arXiv:1805.02513.

Taylor, G. W., Fergus, R., LeCun, Y., & Bregler, C. (2010). Convolutional learning of spatio-temporal features. In K. Daniilidis, P. Maragos, & N. Paragios (Eds.), *Computer vision - ECCV 2010* (pp. 140-153).

Thida, M., Yong, Y. L., Climent-Pérez, P., Eng, H.-l., & Remagnino, P. (2013). A literature review on video analytics of crowded scenes. In *Intelligent multimedia surveillance* (pp. 17-36). Springer.

Tian, Y., Luo, P., Wang, X., & Tang, X. (2015). Deep learning strong parts for pedestrian detection. In *Proceedings of the IEEE international conference on computer vision* (pp. 1904-1912).

Tomasi, C., & Kanade, T. (1991). Detection and tracking of point features. *Technical report, International Journal of Computer Vision*.

Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., & Paluri, M. (2018). A closer look at spatiotemporal convolutions for action recognition. In *Proceedings of the IEEE conference on computer vision and pattern recognition* (pp. 6450-6459).

TRB (2000). Highway capacity manual. Transportation Research Board, National Research Council, Washington, DC.

Tripathi, G., Singh, K., & Vishwakarma, D. (2018). Convolutional neural networks for crowd behaviour analysis: a survey. *The Visual Computer*, 1-24.

UCSD (2013). UCSD Anomaly dataset. URL http://www.svcl.ucsd.edu/projects/ anomaly/dataset.htm.

Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. *International Journal of Computer Vision*, **104**(2), 154-171.

Ullah, H., & Conci, N. (2012). Crowd motion segmentation and anomaly detection via multi-label optimization. In *ICPR workshop on pattern recognition and crowd analysis vol. 75*.

Ullah, H., Khan, S. D., Ullah, M., Cheikh, F. A., & Uzair, M. (2019). Two stream model for crowd video classification. In *2019 8th european workshop on visual information processing (EVIP)* (pp. 93-98). IEEE.

Ullah, M., Ullah, H., Conci, N., & De Natale, F. G. (2016). Crowd behavior identification. In *2016 IEEE international conference on image processing (ICIP)* (pp. 1195-1199). IEEE.

Vahora, S., & Chauhan, N. (2018). Deep neural network model for group activity recognition using contextual relationship. *Engineering Science and Technology, An International Journal*.

Vascon, S., & Bazzani, L. (2017). Group detection and tracking using sociological features. In *Group and crowd behavior for computer vision* (pp. 29-66). Elsevier.

Vishwakarma, S., & Agrawal, A. (2013). A survey on activity recognition and behavior understanding in video surveillance. *The Visual Computer*, **29**(10), 983-1009.

Voon, W. P., Mustapha, N., Affendey, L. S., & Khalid, F. (2019). Collective interaction filtering approach for detection of group in diverse crowded scenes. *TIIS*, **13**(2), 912-928.

Walach, E., & Wolf, L. (2016). Learning to count with CNN boosting. In *European conference on computer vision* (pp. 660-676).

Walia, G. S., & Kapoor, R. (2016). Recent advances on multique object tracking: a survey. *Artificial Intelligence Review*, **46**(1), 1-39.

Wan, J., & Chan, A. (2019). Adaptive density map generation for crowd counting. In *Proceedings of the IEEE international conference on computer vision* (pp. 1130-1139).

Wan, J., Kumar, N. S., & Chan, A. B. (2020). Fine-grained crowd counting. arXiv: 2007.06146.

Wang, Z., Cheng, C., & Wang, X. (2018). A fast crowd segmentation method. In *2018 international conference on audio, language and image processing (ICALIP)* (pp. 242-245).

Wang, Q., Gao, J., Lin, W., & Yuan, Y. (2019). Learning from synthetic data for crowd counting in the wild. In *Proceedings of the IEEE conference on computer vision and pattern recognition* (pp. 8198-8207).

Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2011). Action recognition by dense trajectories. In *CVPR 2011* (pp. 3169-3176). IEEE.

Wang, W., Lin, W., Chen, Y., Wu, J., Wang, J., & Sheng, B. (2014). Finding coherent motions and semantic regions in crowd scenes: A diffusion and clustering approach. In *European conference on computer vision* (pp. 756-771). Springer.

Wang, X., Ma, K. T., Ng, G.-W., & Grimson, W. E. L. (2011). Trajectory analysis and semantic region modeling using nonparametric hierarchical bayesian models. *International Journal of Computer Vision*, **95**(3), 287-312.

Wang, H., & O'Sullivan, C. (2016). Globally continuous and non-Markovian crowd activity analysis from videos. In *European conference on computer vision* (pp. 527-544). Springer.

Wang, L., Shi, J., Song, G., & Shen, I.-f. (2007). Object detection combining recognition and segmentation. In *Asian conference on computer vision* (pp. 189-199).

Wang, X., Tieu, K., & Grimson, E. (2006). Learning semantic scene models by trajectory analysis. In *European conference on computer vision* (pp. 110-123). Springer.

Wang, H., Ullah, M. M., Klaser, A., Laptev, I., & Schmid, C. (2009). Evaluation of local spatio-temporal features for action recognition. In *BMVC 2009-British machine vision conference* 124-1.

Wei, X., Du, J., Xue, Z., Liang, M., Geng, Y., Xu, X., et al. (2020). A very deep two-stream network for crowd type recognition. *Neurocomputing*, **396**, 522-533.

Wojke, N., Bewley, A., & Paulus, D. (2017). Simple online and realtime tracking with a deep association metric. In *Image processing (ICIP), 2017 IEEE international conference on* (pp. 3645-3649). IEEE.

Wu, H., Sankaranarayanan, A. C., & Chellappa, R. (2010). Online empirical evaluation of tracking algorithms. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, **32**(8), 1443-1458.

Wu, M., & Schölkopf, B. (2007). A local learning approach for clustering. In *Advances in neural information processing systems* (pp. 1529-1536).

Wu, S., Yang, H., Zheng, S., Su, H., Fan, Y., & Yang, M.-H. (2017). Crowd behavior analysis via curl and divergence of motion trajectories. *International Journal of Computer Vision*, **123**(3), 499-519.

Xie, S., Zhang, X., & Cai, J. (2019). Video crowd detection and abnormal behavior model detection based on machine learning method. *Neural Computing and Applications*, **31**(1), 175-184.

Yamaguchi, K., Berg, A. C., Ortiz, L. E., & Berg, T. L. (2011). Who are you with and where are you going? In *CVPR 2011* (pp. 1345-1352). IEEE.

Yan, L., Zhu, M., & Yu, C. (2019). Crowd video captioning. arXiv:1911.05449.

Yeffet, L., & Wolf, L. (2009). Local trinary patterns for human action recognition. In *Computer vision, 2009 IEEE 12th international conference on* (pp. 492-497).

You, Q., & Jiang, H. (2018). Action4D: Real-time action recognition in the crowd and clutter. arXiv:1806.02424.

Yuan, Y., Lu, Y., & Wang, Q. (2017). Tracking as a whole: Multi-target tracking by modeling group behavior with sequential detection. *IEEE Transactions on Intelligent Transportation Systems*, **18**(12), 3339-3349.

Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In *European conference on computer vision* (pp. 818-833).

Zhan, B., Monekosso, D. N., Remagnino, P., Velastin, S. A., & Xu, L. Q. (2008). Crowd analysis: A survey. *Machine Vision and Applications*, **19**(5-6), 345-357.

Zhang, L., Lin, L., Liang, X., & He, K. (2016). Is faster R-CNN doing well for pedestrian detection? In *European conference on computer vision* (pp. 443-457).

Zhang, J., Liu, Y., & Jiang, J. (2018). Tensor learning and automated rank selection for regression-based video classification. *Multimedia Tools and Applications*, **77**(22), 29213-29230.

Zhang, X., Zhang, Q., Hu, S., Guo, C., & Yu, H. (2018). Energy level-based abnormal crowd behavior detection. *Sensors*, **18**(2), 423.

Zhang, Y., Zhou, D., Chen, S., Gao, S., & Ma, Y. (2016). Single-image crowd counting via multi-column convolutional neural network. In *Proceedings of the IEEE conference on computer vision and pattern recognition* (pp. 589-597).

Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., et al. (2016). Mars: A video benchmark for large-scale person re-identification. In *European conference on computer vision* (pp. 868-884).

Zhou, B., Tang, X., & Wang, X. (2012). Coherent filtering: Detecting coherent motions from crowd clutters. In *European conference on computer vision* (pp. 857-871). Springer.

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In *Proceedings of the IEEE international conference on computer vision* (pp. 2223-2232).

Zitnick, C. L., & Dollár, P. (2014). Edge boxes: Locating object proposals from edges. In *European conference on computer vision* (pp. 391-405).

Zitouni, M. S., Bhaskar, H., & Al-Mualla, M. E. (2016). Robust background modeling and foreground detection using dynamic textures. In *VISIGRAPP (4: VISAPP)* (pp. 403-410).

Zitouni, M. S., Sluzek, A., & Bhaskar, H. (2020). Towards understanding socio-cognitive behaviors of crowds from visual surveillance data. *Multimedia Tools and Applications*, **79**(3), 1781-1799.