Fetching the paper…
Reading the bibliography…
As humans we possess an intuitive ability for navigation which we master through years of practice; however existing approaches to model this trait for diverse tasks including monitoring pedestrian flow and detecting abnormal events have been limited by using a variety of hand-crafted features.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R.J., 1992 · 1992
Earlier work this paper cites.
Social force model for pedestrian dynamics
Helbing, D., Molnár, P., 1995 · 1995
Earlier work this paper cites.
A density-based algorithm for discovering clusters in large spatial databases with noise, pp. 226–231
Ester, M., peter Kriegel, H., Sander, J., Xu, X., 1996 · 1996
Earlier work this paper cites.
Trajectory pattern mining, in: ACM SIGKDD, pp. 330–339
Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D., 2007 · 2007
Earlier work this paper cites.
Trajectory clustering: A partition-and-group framework, in: ACM SIGMOD, pp. 593–604
Lee, J.G., Han, J., Whang, K.Y., 2007 · 2007
Earlier work this paper cites.
Trajectory analysis and semantic region modeling using a nonparametric bayesian model., in: CVPR, pp. 1–8
Wang, X., Ma, K.T., Ng, G.W., Grimson, W.E.L., 2008 · 2008
Earlier work this paper cites.
A markov clustering topic model for mining behaviour in video., in: ICCV, pp. 1165–1172
Hospedales, T.M., Gong, S., Xiang, T., 2009 · 2009
Earlier work this paper cites.
Statistical models of pedestrian behaviour in the forum
Majecka, B., 2009 · 2009
Earlier work this paper cites.
Learning trajectory patterns by clustering: Experimental studies and comparative evaluation., in: CVPR, pp. 312–319
Morris, B., Trivedi, M.M., 2009 · 2009
Earlier work this paper cites.
Improving data association by joint modeling of pedestrian trajectories and groupings., in: ECCV, pp. 452–465
Pellegrini, S., Ess, A., Gool, L.J.V., 2010 · 2010
Earlier work this paper cites.
Sequential deep learning for human action recognition, in: HBU, pp. 29–39
Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A., 2011 · 2011
Cited alongside, same era.
Extracting and locating temporal motifs in video scenes using a hierarchical non parametric bayesian model., in: CVPR, pp. 3233–3240
Emonet, R., Varadarajan, J., Odobez, J.M., 2011 · 2011
Cited alongside, same era.
Who are you with and where are you going?, in: CVPR, pp. 1345–1352
Yamaguchi, K., Berg, A.C., Ortiz, L.E., Berg, T.L., 2011 · 2011
Cited alongside, same era.
Unusual scene detection using distributed behaviour model and sparse representation
Xu, J., Denman, S., Fookes, C.B., Sridharan, S., 2012 · 2012
Cited alongside, same era.
Socially-aware large-scale crowd forecasting., in: CVPR, pp. 2211–2218
Alahi, A., Ramanathan, V., Li, F.F., 2014 · 2014
Cited alongside, same era.
Unsupervised feature learning from temporal data
Goroshin, R., Bruna, J., Tompson, J., Eigen, D., LeCun, Y., 2015 · 2015
Later among the works it cites.
Guiding the long-short term memory model for image caption generation, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 2407–2415
Jia, X., Gavves, E., Fernando, B., Tuytelaars, T., 2015 · 2015
Later among the works it cites.
Action recognition using visual attention
Sharma, S., Kiros, R., Salakhutdinov, R., 2015 · 2015
Later among the works it cites.
Show, attend and tell: Neural image caption generation with visual attention., in: ICML, pp. 77–81
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A.C., Salakhutdinov, R., Zemel, R.S., Bengio, Y., 2015 · 2015
Later among the works it cites.
Describing videos by exploiting temporal structure, in: ICCV, pp. 4507 – 4515
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bahdanau, D., Cho, K., Bengio, Y., 2014 · 2014
Cited alongside, same era.
Unsupervised feature learning for 3d scene labeling, in: ICRA, pp. 3050–3057
Lai, K., Bo, L., Fox, D., 2014 · 2014
Cited alongside, same era.
ST-HMP: unsupervised spatio-temporal feature learning for tactile data, in: ICRA, pp. 2262–2269
Madry, M., Bo, L., Kragic, D., Fox, D., 2014 · 2014
Cited alongside, same era.
Recurrent models of visual attention, in: Advances in neural information processing systems, pp. 2204–2212
Mnih, V., Heess, N., Graves, A., et al., 2014 · 2014
Cited alongside, same era.
Understanding pedestrian behaviors from stationary crowd groups., in: CVPR, pp. 3488 – 3496
Yi, S., Li, H., Wang, X., 2015a
Cited in the paper.
Understanding pedestrian behaviors from stationary crowd groups, in: CVPR, pp. 3488 – 3496
Yi, S., Li, H., Wang, X., 2015b
Cited in the paper.
Yao, L., Torabi, A., Cho, K., Ballas, N., Pal, C., Larochelle, H., Courville, A., 2015 · 2015
Later among the works it cites.
Attentionnet: Aggregating weak directions for accurate object detection., in: ICCV, pp. 2659–2667
Yoo, D., Park, S., Lee, J.Y., Paek, A.S., Kweon, I.S., 2015 · 2015
Later among the works it cites.
Learning collective crowd behaviors with dynamic pedestrian-agents
Zhou, B., Tang, X., Wang, X., 2015 · 2015
Later among the works it cites.
Social lstm: Human trajectory prediction in crowded spaces, in: CVPR
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S., 2016 · 2016
Later among the works it cites.
Anticipating human activities using object affordances for reactive robotic response., in: IROS, p. 2071
Koppula, H.S., Saxena, A., 2013 · 2071
Closest in time.