The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Learning an Image-Based Motion Context for Multiple People Tracking
Abstract
We present a novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals. At the core of our model lies a learned dictionary of interaction feature strings which capture relationships between the motions of targets. These feature strings, created from low-level image features, lead to a much richer representation of the physical interactions between targets compared to hand-specified social force models that previous works have introduced for tracking. One disadvantage of using social forces is that all pedestrians must be detected in order for the forces to be applied, while our method is able to encode the effect of undetected targets, making the tracker more robust to partial occlusions. The interaction feature strings are used in a Random Forest framework to track targets according to the features surrounding them. Results on six publicly available sequences show that our method outperforms state-of-the-art approaches in multiple people tracking.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Social force model for pedestrian dynamics
Helbing, D., & Molnár, P. (1995) · Physical Review E · doi:10.1103/physreve.51.4282 · Cited by 6,845
Real-time human pose recognition in parts from single depth images
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995316 · Cited by 3,491
Real-time human pose recognition in parts from single depth images
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., & Moore, R. (2013) · Communications of the ACM · doi:10.1145/2398356.2398381 · Cited by 2,044
You'll never walk alone: Modeling social behavior for multi-target tracking
Pellegrini, S., Ess, A., Schindler, K., & van Gool, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459260 · Cited by 1,683 · cited 6×
“Since odometry information is found automatically and is prone to errors, this severely affects the efficacy of [19] and [25].”
BackgroundMethodInfluentialAbnormal crowd behavior detection using social force model
Mehran, R., Oyama, A., & Shah, M. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206641 · Cited by 1,672
Multiple Object Tracking Using K-Shortest Paths Optimization
Berclaz, J., Fleuret, F., Turetken, E., & Fua, P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.21 · Cited by 1,015
Global data association for multi-object tracking using network flows
Zhang, L., Li, Y., & Nevatia, R. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587584 · Cited by 925
Globally-optimal greedy algorithms for tracking a variable number of objects
Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995604 · Cited by 790
Cited by
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00240 · Cited by 2,466
Self-citationMOT16: A Benchmark for Multi-Object Tracking
Milan, A., Leal-Taixe, L., Reid, I., Roth, S., & Schindler, K. (2016) · arXiv · doi:10.48550/arxiv.1603.00831 · Cited by 1,309
Towards Real-Time Multi-Object Tracking
Wang, Z., Zheng, L., Liu, Y., Li, Y., & Wang, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_7 · Cited by 1,195
Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data
Salzmann, T., Ivanovic, B., Chakravarty, P., & Pavone, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58523-5_40 · Cited by 1,076
TrackFormer: Multi-Object Tracking with Transformers
Meinhardt, T., Kirillov, A., Leal-Taixe, L., & Feichtenhofer, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00864 · Cited by 1,081
“Learning a motion model from data [24] accomplishes track association between frames [63].”
BackgroundMethodSelf-citation
Metadata
- Authors
- Leal-Taixe, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., & Savarese, S.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 3542-3549
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2020209171
- Cited by
- 280
- References
- 37
- FWCI
- 13.70
- Type
- conference paper
- Language
- en
- Open access
- closed
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