Abstract
In this paper,we present a visual support system the visually impaired. Our detection algorithm is based on the well known Histograms of Oriented Gradients (HOG) method, due to its high detection rate and versatility [5]. However, the accuracy of object recognition rate is reduced because of high false detection rate. In order to solve that, multiple parts model and triple phase detection have been implemented. These additional filtering stages were conducted by separate action on different area of the sample, considering deformations and translations. We demonstrated that this approach has raised the accuracy and speed of calculation. Through an evaluation experiment based on a large dataset, we found that false detection has been improved by 18.9% in respect to standard HOG detectors. Experimental tests have also shown the system ability to estimate the distance of the pedestrian by the use of a simple perspective model. The system has been tested on several photographic datasets and have shown excellent performances also in ambiguous cases.
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Brasnett, P., Mihaylova, L., Bull, D., Canagarajah, N.: Sequential monte carlo tracking by fusing multiple cues in video sequences. Image vision Computing 25(8), 1217–1227 (2007)
Cuevas, E., Zaldiver, D., Rojas, R.: Kalman filter for vision tracking. Fachbereich Mathematik und Informatic. Technical Report B, Freie Universitat Berlin (2005)
Hoiem, D., Efros, A.A., Hebert, M.: Putting objects in perspective. Pattern Analysis and Machine Intelligence 2 (2006)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Suard, F., Rakotomamonjy, A., Bensrhair, A., Broggi, A.: Pedestrian detection using infrared images and histograms of oriented gradients. In: Proc. IEEE Conf. Intell. Vehicles, pp. 206–212 (2006)
Felzenszwalb, P., Girschick, R., McAllester, D.: Cascade object detection with deformable part models. In: CVPR, pp. 1–8 (2010)
Han, F., Shan, Y., Cekander, R., Sawhney, H.S., Kumar, R.: A two-stage approach to people and vehicle detection with hog-based svm. In: Performance Metrics for Intelligent Systems Workshop in conjunction with the IEEE Safety, Security, and Rescue Robotics Conference, pp. 134–136 (2006)
Helal, A., Moore, S., Ramachandran, B.: Drishti: An integrated navigation system for the visually impaired and disabled. In: Fifth International Symposium on Wearable Computers (ISWC 2001), pp. 149–156 (2001)
Juan, L., Gwun, O.: A comparison of sift, pca-sift and surf. International Journal of Image Processing 3, 143–152 (2009)
Montabone, S., Soto, A.: Human detection using a mobile platform and novel features derived from a visual saliency mechanism. Image and Vision Computing 28(3), 391–402 (2010)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. Pattern Analysis and Machine Intelligence, 1627–1645 (2010)
Zhu, Q., Avidan, S., Yeh, M.C., Cheng, K.T.: Fast human detection using a cascade of histograms of oriented gradients. Mitsubishi Electric Research Laboratories, pp. 1491–1498 (2006)
Yan, X., Luo, Y.: Recognizing human actions using a new descriptor based on spatial-temporal interest points and weighted-output classifier. Neurocomputing 87, 51–61 (2012)
Zhang, T., Liu, S., Xu, C., Lu, H.: Boosted multi-class semi-supervised learning for human action recognition. Pattern Recognition 44(10-11, SI), 2334–2342 (2011)
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Takahisa, K., Sun, Z., Micheletto, R. (2013). A Fast and Precise HOG-Adaboost Based Visual Support System Capable to Recognize Pedestrian and Estimate Their Distance. In: Petrosino, A., Maddalena, L., Pala, P. (eds) New Trends in Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41190-8_3
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DOI: https://doi.org/10.1007/978-3-642-41190-8_3
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