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Adaptive Structural Model for Video Based Pedestrian Detection

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Computer Vision – ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

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Abstract

The performance of generic pedestrian detector usually declines seriously for videos in novel scenes, which is one of the major bottlenecks for current pedestrian detection techniques. The conventional works improve pedestrian detection in video by mining new instances from detections and adapting the detector according to the collected instances. However, when treating the two tasks separately, the detector adaptation suffers from the defective output of instance mining. In this paper, we propose to jointly handle the instance mining and detector adaption using an adaptive structural model. The regularization function of the model is applied on detector to prevent overfitting in adaption, and the loss function is designed to evaluate the combination of mined instances set and detector. Particularly, we extend the Deformable Part Model (DPM) to adaptive DPM, where an adaptive feature transformation defined on low-level HOG cell is learned to reduce the domain shift, and the regularization function for the detector is conducted on the transformation. The loss of the instance set and detector is measured by a cost-flow network structure which incorporates both the appearance of frame-wise detections and their spatio-temporal continuity. We demonstrate an alternating minimization procedure to optimize the model. The proposed method is evaluated on ETHZ, PETS2009 and Caltech datasets, and outperforms baseline DPM by 7 % in terms of mean miss rate.

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Notes

  1. 1.

    http://www.cvg.rdg.ac.uk/PETS2009/.

  2. 2.

    The mean miss rate defined in P. Dollár’s toolbox is used here, which is the average miss rate at 0.0100, 0.0178, 0.0316, 0.0562, 0.1000, 0.1778, 0.3162, 0.5623 and 1.0000 false-positive-per-image.

  3. 3.

    The two videos are selected as they contain more people than other videos.

References

  1. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR. IEEE (2005)

    Google Scholar 

  2. Yan, J., Lei, Z., Yi, D., Li, S.Z.: Multi-pedestrian detection in crowded scenes: A global view. In: CVPR. IEEE (2012)

    Google Scholar 

  3. Maji, S., Berg, A., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: CVPR. IEEE (2008)

    Google Scholar 

  4. Wang, X., Han, T., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: ICCV. IEEE (2009)

    Google Scholar 

  5. Walk, S., Majer, N., Schindler, K., Schiele, B.: New features and insights for pedestrian detection. In: CVPR. IEEE (2010)

    Google Scholar 

  6. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI (2010)

    Google Scholar 

  7. Park, D., Ramanan, D., Fowlkes, C.: Multiresolution models for object detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 241–254. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  8. Yan, J., Zhang, X., Lei, Z., Liao, S., Li, S.Z.: Robust multi-resolution pedestrian detection in traffic scenes. In: CVPR. IEEE (2013)

    Google Scholar 

  9. Huang, C., Nevatia, R.: High performance object detection by collaborative learning of joint ranking of granules features. In: CVPR. IEEE (2010)

    Google Scholar 

  10. Dollár, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: BMVC 2010 (2010)

    Google Scholar 

  11. Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: CVPR. IEEE (2012)

    Google Scholar 

  12. Dollár, P., Appel, R., Kienzle, W.: Crosstalk cascades for frame-rate pedestrian detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 645–659. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  13. Yan, J., Lei, Z., Wen, L., Li, S.Z.: The fastest deformable part model for object detection. In: CVPR (2014)

    Google Scholar 

  14. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art. TPAMI (2012)

    Google Scholar 

  15. Yang, M., Zhu, S., Lv, F., Yu, K.: Correspondence driven adaptation for human profile recognition. In: CVPR. IEEE (2011)

    Google Scholar 

  16. Sharma, P., Huang, C., Nevatia, R.: Unsupervised incremental learning for improved object detection in a video. In: CVPR. IEEE (2012)

    Google Scholar 

  17. Wang, X., Hua, G., Han, T.X.: Detection by detections: Non-parametric detector adaptation for a video. In: CVPR. IEEE (2012)

    Google Scholar 

  18. Tang, K., Ramanathan, V., Fei-Fei, L., Koller, D.: Shifting weights: Adapting object detectors from image to video. In: NIPS (2012)

    Google Scholar 

  19. Wang, M., Wang, X.: Automatic adaptation of a generic pedestrian detector to a specific traffic scene. In: CVPR. IEEE (2011)

    Google Scholar 

  20. Wang, M., Li, W., Wang, X.: Transferring a generic pedestrian detector towards specific scenes. In: CVPR. IEEE (2012)

    Google Scholar 

  21. Sharma, P., Nevatia, R.: Efficient detector adaptation for improved object detection in a video. In: CVPR. IEEE (2013)

    Google Scholar 

  22. Yang, Y., Shu, G., Shah, M.: Semi-supervised learning of feature hierarchies for object detection in a video. In: CVPR. IEEE (2013)

    Google Scholar 

  23. Enzweiler, M., Gavrila, D.: Monocular pedestrian detection: Survey and experiments. TPAMI (2009)

    Google Scholar 

  24. Geronimo, D., Lopez, A., Sappa, A., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. PAMI (2010)

    Google Scholar 

  25. Roth, P.M., Sternig, S., Grabner, H., Bischof, H.: Classifier grids for robust adaptive object detection. In: CVPR. IEEE (2009)

    Google Scholar 

  26. Pang, J., Huang, Q., Yan, S., Jiang, S., Qin, L.: Transferring boosted detectors towards viewpoint and scene adaptiveness. TIP (2011)

    Google Scholar 

  27. Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  28. Kulis, B., Saenko, K., Darrell, T.: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In: CVPR. IEEE (2011)

    Google Scholar 

  29. Gao, T., Stark, M., Koller, D.: What makes a good detector? – Structured priors for learning from few examples. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 354–367. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  30. Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: An unsupervised approach. In: ICCV. IEEE (2011)

    Google Scholar 

  31. Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR. IEEE (2012)

    Google Scholar 

  32. Pirsiavash, H., Ramanan, D.: Steerable part models. In: CVPR. IEEE (2012)

    Google Scholar 

  33. Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR. IEEE (2008)

    Google Scholar 

  34. Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR. IEEE (2011)

    Google Scholar 

  35. Berclaz, J., Fleuret, F., Fua, P.: Multiple object tracking using flow linear programming. In: PETS-Winter. IEEE (2009)

    Google Scholar 

  36. Jiang, H., Fels, S., Little, J.J.: A linear programming approach for multiple object tracking. In: CVPR. IEEE (2007)

    Google Scholar 

  37. Yang, B., Huang, C., Nevatia, R.: Learning affinities and dependencies for multi-target tracking using a crf model. In: CVPR. IEEE (2011)

    Google Scholar 

  38. Andriyenko, A., Schindler, K.: Globally optimal multi-target tracking on a hexagonal lattice. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 466–479. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  39. Wen, L., Li, W., Yan, J., Lei, Z., Yi, D., Li, S.Z.: Multiple target tracking based on undirected hierarchical relation hypergraph (2014)

    Google Scholar 

  40. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2009)

    MATH  Google Scholar 

  41. Ess, A., Leibe, B., Schindler, K., van Gool, L.: A mobile vision system for robust multi-person tracking. In: CVPR. IEEE (2008)

    Google Scholar 

  42. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. IJCV 63(2), 153–161 (2005)

    Article  Google Scholar 

  43. Wojek, C., Schiele, B.: A performance evaluation of single and multi-feature people detection. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 82–91. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  44. Lin, Z., Davis, L.S.: A pose-invariant descriptor for human detection and segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 423–436. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  45. Dollár, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: CVPR. IEEE (2007)

    Google Scholar 

  46. Schwartz, W., Kembhavi, A., Harwood, D., Davis, L.: Human detection using partial least squares analysis. In: ICCV. IEEE (2009)

    Google Scholar 

  47. Bar-Hillel, A., Levi, D., Krupka, E., Goldberg, C.: Part-based feature synthesis for human detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 127–142. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  48. Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC (2009)

    Google Scholar 

  49. Dubout, C., Fleuret, F.: Exact acceleration of linear object detectors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 301–311. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

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Acknowledgement

This work was supported by the Chinese National Natural Science Foundation Projects #61105023, #61103156, #61105037, #61203267, #61375037, #61473291, National Science and Technology Support Program Project #2013BAK02B01, Chinese Academy of Sciences Project No. KGZD-EW-102-2, and AuthenMetric R&D Funds.

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Authors and Affiliations

  1. Center for Biometrics and Security Research and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Junjie Yan, Bin Yang, Zhen Lei & Stan Z. Li

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  1. Junjie Yan
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  2. Bin Yang
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  3. Zhen Lei
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  4. Stan Z. Li
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Corresponding author

Correspondence to Zhen Lei .

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Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Yan, J., Yang, B., Lei, Z., Li, S.Z. (2015). Adaptive Structural Model for Video Based Pedestrian Detection. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-16865-4_14

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