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The Real Time Human Detection

Vikas Ramashankar Pandey, Saurav Dilip Kale, Diksha Bhalerao


For many computer vision applications, including visual surveillance, picture retrieval, and driver assistance systems, human detection is crucial. The Project Real Time Human Detection addresses the issues of face recognition, face detection, and human detection. In a given photograph or video, the project is capable of identifying a human and its face. A crucial component of video surveillance is person detection, a technique that predetermines the human forms in images and videos and ignores everything else which plays an irreplaceable role in video Surveillance. The Histogram of Oriented Gradient (HOG) based Descriptor significantly perform better than any other gradient based descriptor. This field is one of the most popular fields of study for many researchers nowadays as it has been already seen its implementation in self-driving car and for security purpose. The use of machine learning and image processing, using Convolution Neural Networks (CNN) and OpenCV, an open-source computer vision library, is the foundation of the entire effort.

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Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems. 2015; 28.

Kim D, Jun B. Accurate Face and Human Detection Using Hybrid Local Transform Features. Intheory and Applications of Smart Cameras 2016 (pp. 157–185). Springer, Dordrecht..

Liang CW, Juang CF. Moving object classification using local shape and HOG features in wavelet-transformed space with hierarchical SVM classifiers. Applied Soft Computing. 2015 Mar 1; 28: 483–97.

Barbu T. Novel approach for moving human detection and tracking in static camera video sequences. Proceedings of the Romanian Academy, Series A. 2012 Jul 1; 13 (3): 269–77.

Conde C, Moctezuma D, De Diego IM, Cabello E. Hogg: Gabor and hog-based human detection for surveillance in non-controlled environments. Neurocomputing. 2013 Jan 16; 100: 19–30.

Dalal N, Triggs B. Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) 2005 Jun 20 (Vol. 1, pp. 886–893). IEEE.

Mohan A, Papageorgiou C, Poggio T. Example-based object detection in images by components. IEEE transactions on pattern analysis and machine intelligence. 2001 Apr; 23 (4): 349–61.

Gavrila DM, Philomin V. Real-time object detection using distance transforms. Inproc. Intelligent Vehicles Conf 1998 Oct (Vol. 998).

Viola P, Jones MJ. Robust real-time face detection. International journal of computer vision. 2004 May; 57 (2): 137–54.

Wu J, Geyer C, Rehg JM. Real-time human detection using contour cues. In2011 IEEE international conference on robotics and automation 2011 May 9 (pp. 860–867). IEEE.



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