Abstract
Detector accuracy is by any means the key focus in most existing pedestrian detection algorithms especially for clutter scenes. However, it is not always necessary, while sometimes over-fitted, to directly leverage such detectors in scenarios with simple scene compositions. To this end, limited work has done on a systematic detector simplification towards balancing its speed and accuracy. In this paper, we study this problem by investigating two mutually correlated issues, i.e. fast edge-based feature extraction and detector score computation. For handling the first issue, a simple Structured Local Edge Pattern (SLEP) is proposed to extract and encode local edge cues, extremely effectively, into a histogram. For the second, an integral image based acceleration is proposed toward fast classifier score computation by transforming the classifier score into a linear sum of weights. Experimental results on CASIA gait recognition dataset show that our proposed method is highly efficient than most existing detectors, which even faster than the practical OpenCV pedestrian detector.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Geronimo, D., Lopez, A.M., Sappa, A.D., et al.: Survey of pedestrian detection for advanced driver assistance systems. Pattern Analysis and Machine Intelligence (PAMI) 32, 1239–1258 (2010)
Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: A review. ACM Computing Surveys (CSUR)Â 43, 16 (2011)
Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28, 976–990 (2010)
Weinland, D., Ronfard, R., Boyer, E.: A survey of vision-based methods for action representation, segmentation and recognition. Computer Vision and Image Understanding 115, 224–241 (2011)
Hu, W., Tan, T., Wang, L., et al.: A survey on visual surveillance of object motion and behaviors. Systems, Man, and Cybernetics, Part C: Applications and Reviews 34, 334–352 (2004)
Gao, Y., Wang, M., Tao, D., et al.: 3D Object Retrieval and Recognition with Hypergraph Analysis. IEEE Transactions on Image Processing 21, 4290–4303 (2012)
Gao, Y., Tang, J., Hong, R., et al.: Camera Constraint-Free View-Based 3D Object Retrieval. IEEE Transactions on Image Processing 21, 2269–2281 (2012)
Dollar, P., Wojek, C., Schiele, B., et al.: Pedestrian detection: An evaluation of the s-tate of the art. Pattern Analysis and Machine Intelligence 34, 743–761 (2012)
Oren, M., Papageorgiou, C., Sinha, P., et al.: Pedestrian detection using wavelet templates. Computer Vision and Pattern Recognition, 193–199 (1997)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. Computer Vision and Pattern Recognition 1, 886–893 (2005)
Szarvas, M., Sakai, U., Ogata, J.: Real-time pedestrian detection using lidar and convolutional neural networks. In: Intelligent Vehicles Symposium, pp. 213–218 (2006)
Wang, X., Han, T.X., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 32–39 (2009)
Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. Computer Vision and Pattern Recognition, 1–8 (2008)
Zhu, Q., Yeh, M.C., Cheng, K.T., et al.: Fast human detection using a cascade of histograms of oriented gradients. Computer Vision and Pattern Recognition 2, 1491–1498 (2006)
Gall, J., Yao, A., Razavi, N., et al.: Hough forests for object detection, tracking, and action recognition. Pattern Analysis and Machine Intelligence 33, 2188–2202 (2011)
Lampert, C.H., Blaschko, M.B., Hofmann, T.: Efficient subwindow search: A branch and bound framework for object localization. Pattern Analysis and Machine Intelligence 31, 2129–2142 (2009)
Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. International Journal of Computer Vision 77, 259–289 (2008)
Moritz, C.T., Farley, C.T.: Human hopping on damped surfaces: strategies for adjusting leg mechanics. Proceedings of the Royal Society of London. Series B: Biological Sciences 270, 1741–1746 (2003)
Cheng, Y.C., Chen, S.Y.: Image classification using color, texture and regions. Image and Vision Computing 21, 759–776 (2003)
Wang, L., Hu, W., Tan, T.: A new attempt to gait-based human identification. Pattern Recognition 1, 115–118 (2002)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yuan, DD., Dong, J., Su, SZ., Li, SZ., Ji, RR. (2014). Pursuing Detector Efficiency for Simple Scene Pedestrian Detection. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8326. Springer, Cham. https://doi.org/10.1007/978-3-319-04117-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-04117-9_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04116-2
Online ISBN: 978-3-319-04117-9
eBook Packages: Computer ScienceComputer Science (R0)