nach oben

International Journal of Computer Vision

Erschienen in:

30.08.2020

Pixel-Wise Crowd Understanding via Synthetic Data

verfasst von: Qi Wang, Junyu Gao, Wei Lin, Yuan Yuan

Erschienen in: International Journal of Computer Vision | Ausgabe 1/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Crowd analysis via computer vision techniques is an important topic in the field of video surveillance, which has wide-spread applications including crowd monitoring, public safety, space design and so on. Pixel-wise crowd understanding is the most fundamental task in crowd analysis because of its finer results for video sequences or still images than other analysis tasks. Unfortunately, pixel-level understanding needs a large amount of labeled training data. Annotating them is an expensive work, which causes that current crowd datasets are small. As a result, most algorithms suffer from over-fitting to varying degrees. In this paper, take crowd counting and segmentation as examples from the pixel-wise crowd understanding, we attempt to remedy these problems from two aspects, namely data and methodology. Firstly, we develop a free data collector and labeler to generate synthetic and labeled crowd scenes in a computer game, Grand Theft Auto V. Then we use it to construct a large-scale, diverse synthetic crowd dataset, which is named as “GCC Dataset”. Secondly, we propose two simple methods to improve the performance of crowd understanding via exploiting the synthetic data. To be specific, (1) supervised crowd understanding: pre-train a crowd analysis model on the synthetic data, then fine-tune it using the real data and labels, which makes the model perform better on the real world; (2) crowd understanding via domain adaptation: translate the synthetic data to photo-realistic images, then train the model on translated data and labels. As a result, the trained model works well in real crowd scenes.Extensive experiments verify that the supervision algorithm outperforms the state-of-the-art performance on four real datasets: UCF_CC_50, UCF-QNRF, and Shanghai Tech Part A/B Dataset. The above results show the effectiveness, values of synthetic GCC for the pixel-wise crowd understanding. The tools of collecting/labeling data, the proposed synthetic dataset and the source code for counting models are available at https://gjy3035.github.io/GCC-CL/.

Vorheriger Artikel DeepVS2.0: A Saliency-Structured Deep Learning Method for Predicting Dynamic Visual Attention

Nächster Artikel Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B., & Vijayanarasimhan, S. (2016). Youtube-8m. A large-scale video classification benchmark. arXiv preprint arXiv:1609.08675.

Sam, D. B., Sajjan, N. N., Babu, R. V., & Srinivasan, M. (2018). Divide and grow capturing huge diversity in crowd images with incrementally growing CNN. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3618–3626).

Bak, S., Carr, P., & Lalonde, J. F. (2018). Domain adaptation through synthesis for unsupervised person re-identification. arXiv preprint arXiv:1804.10094.

Cao, X., Wang, Z., Zhao, Y., & Su, F. (2018). Scale aggregation network for accurate and efficient crowd counting. In Proceedings of the European conference on computer vision (pp. 734–750).

Chan, A. B., & Vasconcelos, N. (2009). Bayesian poisson regression for crowd counting. In 2009 IEEE 12th international conference on computer vision (pp. 545–551). IEEE.

Chan, A. B., Liang, Z. S. J., & Vasconcelos, N. (2008). Privacy preserving crowd monitoring: Counting people without people models or tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–7).

Chan, A. B., Morrow, M., & Vasconcelos, N, et al. (2009). Analysis of crowded scenes using holistic properties. In Performance evaluation of tracking and surveillance workshop at CVPR (pp. 101–108).

Chen, K., Loy, C. C., Gong, S., & Xiang, T. (2012). Feature mining for localised crowd counting. In Proceedings of the British machine vision conference (vol. 1, p. 3).

Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213–3223).

Deng, J., Dong, W., Socher R., Li, L. J., Li, K., & Fei-Fei, L. (2009). Imagenet A large-scale hierarchical image database. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 248–255).

Dong, L., Parameswaran. V., Ramesh, V., & Zoghlami, I. (2007). Fast crowd segmentation using shape indexing. In 2007 IEEE 11th international conference on computer vision (pp. 1–8). IEEE.

Dosovitskiy, A.,Ros, G., Codevilla F., Lopez, A., & Koltun, V. (2017). CARLA: An open urban driving simulator. In Proceedings of the 1st annual conference on robot learning (pp. 1–16).

Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2015). The pascal visual object classes challenge: A retrospective. International journal of computer vision, 111(1), 98–136.CrossRef

Fu, M., Xu, P., Li, X., Liu, Q., Ye, M., & Zhu, C. (2015). Fast crowd density estimation with convolutional neural networks. Engineering Applications of Artificial Intelligence, 43, 81–88.CrossRef

Gao, J., Lin, W., Zhao, B., Wang, D., Gao, C., & Wen, J. (2019). C\(^3\) framework. An open-source pytorch code for crowd counting. arXiv preprint arXiv:1907.02724.

Gao, J., Wang, Q., & Li, X. (2019). Pcc net Perspective crowd counting via spatial convolutional network. IEEE Transactions on Circuits and Systems for Video Technology,. https://doi.org/10.1109/TCSVT.2019.2919139.CrossRef

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Proceedings of the advances in neural information processing systems (pp. 2672–2680).

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, KQ. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708).

Idrees, H., Saleemi, I., Seibert, C., & Shah, M. (2013). Multi-source multi-scale counting in extremely dense crowd images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2547–2554).

Idrees, H., Tayyab, M., Athrey, K., Zhang, D., Al-Maadeed, S., Rajpoot, N., & Shah, M. (2018). Composition loss for counting, density map estimation and localization in dense crowds. arXiv preprint arXiv:1808.01050.

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014). Caffe. Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on multimedia (pp. 675–678). ACM.

Jiang, X., Xiao, Z., Zhang, B., Zhen, X., Cao, X., Doermann, D., & Shao, L. (2019). Crowd counting and density estimation by trellis encoder-decoder networks. In The IEEE conference on computer vision and pattern recognition (CVPR) (pp. 6133–6142).

Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, SN., Rosaen, K., & Vasudevan, R. (2017). Driving in the matrix. Can virtual worlds replace human-generated annotations for real world tasks? In: Proceedings of the IEEE international conference on robotics and automation (pp. 1–8).

Kang, K., & Wang, X. (2014). Fully convolutional neural networks for crowd segmentation. arXiv preprint arXiv:1411.4464.

Kempka, M., Wydmuch, M., Runc, G., Toczek, J., & Jaśkowski, W. (2016). Vizdoom: A doom-based ai research platform for visual reinforcement learning. In 2016 IEEE conference on computational intelligence and games (CIG) (pp. 1–8).

Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

Li, C., Lin, L., Zuo, W., Tang, J., & Yang, M. H. (2018a). Visual tracking via dynamic graph learning. IEEE transactions on pattern analysis and machine intelligence, 41(11), 2770–2782.CrossRef

Li, T., Chang, H., Wang, M., Ni, B., Hong, R., & Yan, S. (2014). Crowded scene analysis: A survey. IEEE Transactions on Circuits and Systems for Video Technology, 25(3), 367–386.CrossRef

Li, W., Mahadevan, V., & Vasconcelos, N. (2013). Anomaly detection and localization in crowded scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(1), 18–32.

Li, X., Chen, M., Nie, F., & Wang, Q. (2017). A multiview-based parameter free framework for group detection. In 31st AAAI conference on artificial intelligence.

Li, Y., Zhang, X., & Chen, D. (2018b). Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1091–1100).

Lian, D., Li, J., Zheng, J., Luo, W., & Gao, S. (2019). Density map regression guided detection network for rgb-d crowd counting and localization. In The IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1821–1830).

Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft coco Common objects in context. In European conference on computer vision (pp. 740–755). Springer.

Liu, J., Gao, C., Meng, D., & Hauptmann, A. G. (2018a). Decidenet. Counting varying density crowds through attention guided detection and density estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5197–5206).

Liu, L., Wang, H., Li, G., Ouyang, W., & Lin, L. (2018b). Crowd counting using deep recurrent spatial-aware network. arXiv preprint arXiv:1807.00601.

Liu, W., Salzmann, M., & Fua, P. (2019). Context-aware crowd counting. In The IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5099–5108).

Liu, X., van de Weijer, J., & Bagdanov, A. D. (2018c). Leveraging unlabeled data for crowd counting by learning to rank. arXiv preprint arXiv:1803.03095.

Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431–3440).

Mahadevan, V., Li, W., Bhalodia, V., & Vasconcelos, N. (2010). Anomaly detection in crowded scenes. In IEEE computer society conference on computer vision and pattern recognition (pp. 1975–1981). IEEE.

Marsden, M., McGuinness, K., Little, S., & O’Connor, NE. (2016). Fully convolutional crowd counting on highly congested scenes. arXiv preprint arXiv:1612.00220.

Mehran, R., Oyama, A., & Shah, M. (2009). Abnormal crowd behavior detection using social force model. In 2009 IEEE conference on computer vision and pattern recognition (pp. 935–942). IEEE.

Onororubio, D., & Lopezsastre, R. J. (2016). Towards perspective-free object counting with deep learning (pp. 615–629).

Pan, X., Shi, J., Luo, P., Wang, X., & Tang, X. (2017). Spatial as deep. Spatial cnn for traffic scene understanding. arXiv preprint arXiv:1712.06080.

Paszke, A., Gross, S., Chintala, S., & Chanan, G. (2017). Pytorch: Tensors and dynamic neural networks in python with strong gpu acceleration.

Popoola, O. P., & Wang, K. (2012). Video-based abnormal human behavior recognition-a review. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 865–878.

Qiu, W., Zhong, F., Zhang, Y., Qiao, S., Xiao, Z., Kim, T. S., et al. (2017). Unrealcv. ACM Multimedia Open Source Software Competition: Virtual worlds for computer vision.CrossRef

Ranjan, V., Le, H., & Hoai, M. (2018). Iterative crowd counting. arXiv preprint arXiv:1807.09959.

Richter, S. R., Vineet, V., Roth, S., & Koltun, V. (2016). Playing for data: Ground truth from computer games. In Proceedings of the European conference on computer vision (pp. 102–118).

Richter, S. R., Hayder, Z., & Koltun, V. (2017). Playing for benchmarks. In Proceedings of the international conference on computer vision (Vol. 2).

Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016). The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3234–3243).

Sam, D. B., Surya, S., & Babu, R. V. (2017). Switching convolutional neural network for crowd counting. In Proceedings of the IEEE conference on computer vision and pattern recognition (Vol. 1, p. 6).

Sam, D. B., Sajjan, N. N., & Babu, R. V. (2018). Divide and grow Capturing huge diversity in crowd images with incrementally growing cnn. arXiv preprint arXiv:1807.09993.

Sam, D. B., Sajjan, N. N., Maurya, H., & Babu, R. V. (2019). Almost unsupervised learning for dense crowd counting. In Proceedings of the 33rd AAAI conference on artificial intelligence, Honolulu, HI, USA (Vol. 27).

Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2018). Airsim High-fidelity visual and physical simulation for autonomous vehicles. InField and service robotics (pp. 621–635). Springer.

Shen, Z., Xu, Y., Ni, B., Wang, M., Hu, J., & Yang, X. (2018). Crowd counting via adversarial cross-scale consistency pursuit. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5245–5254).

Shi, Z., Zhang, L., Liu, Y., Cao, X., Ye, Y., Cheng, M. M., & Zheng, G. (2018). Crowd counting with deep negative correlation learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5382–5390).

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

Sindagi, V. A., & Patel, V. M. (2017a). Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In Proceedings of the IEEE international conference on advanced video and signal based surveillance (pp. 1–6).

Sindagi, V. A., & Patel, V. M. (2017b). Generating high-quality crowd density maps using contextual pyramid cnns. In Proceedings of the IEEE international conference on computer vision (pp. 1879–1888).

Sindagi, V. A., Yasarla, R., & Patel, V. M. (2019). Pushing the frontiers of unconstrained crowd counting: New dataset and benchmark method. In Proceedings of the IEEE international conference on computer vision (pp. 1221–1231).

Sindagi, V. A., Yasarla, R., & Patel, V. M. (2020). Jhu-crowd++. Technical Report: Large-scale crowd counting dataset and a benchmark method.

Walach, E., & Wolf, L. (2016). Learning to count with cnn boosting (pp. 660–676).

Wan, J., Luo, W., Wu, B., Chan, A. B., & Liu, W. (2019). Residual regression with semantic prior for crowd counting. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4036–4045).

Wang, C., Zhang, H., Yang, L., Liu, S., & Cao, X. (2015). Deep people counting in extremely dense crowds. In Proceedings of the 23rd ACM international conference on multimedia (pp. 1299–1302). ACM.

Wang, Q., Chen, M., Nie, F., & Li, X. (2018a). Detecting coherent groups in crowd scenes by multiview clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence,. https://doi.org/10.1109/TPAMI.2018.2875002.CrossRef

Wang, Q., Wan, J., & Yuan, Y. (2018b). Deep metric learning for crowdedness regression. IEEE Transactions on Circuits and Systems for Video Technology, 28(10), 2633–2643.CrossRef

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

Wang, Q., Gao, J., Lin, W., & Li, X. (2020). Nwpu-crowd A large-scale benchmark for crowd counting. arXiv preprint arXiv:2001.03360.

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.CrossRef

Xiong, F., Shi, X., & Yeung, D. Y. (2017). Spatiotemporal modeling for crowd counting in videos. arXiv preprint arXiv:1707.07890.

Yan, Z., Yuan, Y., Zuo, W., Tan, X., Wang, Y., Wen, S., & Ding, E. (2019). Perspective-guided convolution networks for crowd counting. In Proceedings of the IEEE international conference on computer vision (pp. 952–961).

Yuan, Y., Fang, J., & Wang, Q. (2014). Online anomaly detection in crowd scenes via structure analysis. IEEE Transactions on Cybernetics, 45(3), 548–561.CrossRef

Zhang, C., Kang, K., Li, H., Wang, X., Xie, R., & Yang, X. (2016a). Data-driven crowd understanding: A baseline for a large-scale crowd dataset. IEEE Transactions on Multimedia, 18(6), 1048–1061.CrossRef

Zhang, Y., Zhou, D., Chen, S., Gao, S., & Ma, Y. (2016b). 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).

Zhao, M., Zhang, J., Zhang, C., & Zhang, W. (2019). Leveraging heterogeneous auxiliary tasks to assist crowd counting. In The IEEE conference on computer vision and pattern recognition (CVPR) (pp. 12736–12745).

Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593.

Zuo, W., Wu, X., Lin, L., Zhang, L., & Yang, M. H. (2018). Learning support correlation filters for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(5), 1158–1172.CrossRef

Titel: Pixel-Wise Crowd Understanding via Synthetic Data
verfasst von: Qi Wang
Junyu Gao
Wei Lin
Yuan Yuan
Publikationsdatum: 30.08.2020
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 1/2021
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-020-01365-4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2021

View Transfer on Human Skeleton Pose: Automatically Disentangle the View-Variant and View-Invariant Information for Pose Representation Learning

DeepVS2.0: A Saliency-Structured Deep Learning Method for Predicting Dynamic Visual Attention

Image Matching from Handcrafted to Deep Features: A Survey

Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory

A Camera Model for Line-Scan Cameras with Telecentric Lenses

Temporally Coherent General Dynamic Scene Reconstruction