Adapting pedestrian detectors to new domains: A comprehensive review.

Article English OPEN
Htike, KK ; Hogg, DC (2016)
  • Publisher: Elsevier

Successful detection and localisation of pedestrians is an important goal in computer vision which is a core area in Artificial Intelligence. State-of-the-art pedestrian detectors proposed in literature have reached impressive performance on certain datasets. However, it has been pointed out that these detectors tend not to perform very well when applied to specific scenes that differ from the training datasets in some ways. Due to this, domain adaptation approaches have recently become popular in order to adapt existing detectors to new domains to improve the performance in those domains. There is a real need to review and analyse critically the state-of-the-art domain adaptation algorithms, especially in the area of object and pedestrian detection. In this paper, we survey the most relevant and important state-of-the-art results for domain adaptation for image and video data, with a particular focus on pedestrian detection. Related areas to domain adaptation are also included in our review and we make observations and draw conclusions from the representative papers and give practical recommendations on which methods should be preferred in different situations that practitioners may encounter in real-life.
  • References (74)
    74 references, page 1 of 8

    [1] A. Andreopoulos, J. K. Tsotsos, 50 years of object recognition: Directions forward, Computer Vision and Image Understanding (2013) 827-891.

    [2] P. Dolla´r, C. Wojek, B. Schiele, P. Perona, Pedestrian detection: An evaluation of the state of the art, IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (4) (2012) 743-761.

    [3] M. Enzweiler, D. M. Gavrila, Monocular pedestrian detection: Survey and experiments, IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (12) (2009) 2179-2195.

    [4] D. Geronimo, A. M. Lopez, A. D. Sappa, T. Graf, Survey of pedestrian detection for advanced driver assistance systems, IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (7) (2010) 1239-1258.

    [5] N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2005, pp. 886-893.

    [6] R. B. Girshick, P. F. Felzenszwalb, D. A. Mcallester, Object detection with grammar models, in: Proceedings of Advances in Neural Information Processing Systems (NIPS), 2011, pp. 442-450.

    [7] P. Dolla´r, C. Wojek, B. Schiele, P. Perona, Pedestrian detection: A benchmark, in: Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 304-311.

    [8] A. Torralba, A. A. Efros, Unbiased look at dataset bias, in: Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 1521-1528.

    [9] P. M. Roth, S. Sternig, H. Grabner, H. Bischof, Classifier grids for robust adaptive object detection, in: Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 2727-2734.

    [10] M. Wang, W. Li, X. Wang, Transferring a generic pedestrian detector towards specific scenes, in: Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3274-3281.

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