This is the part I of the work that I did for my master thesis (part II). In this work first, I computed HOG (Histogram of oriented gradients) on my images and then sent the computed histogram to a linear SVM (support vector machine). The SVM was trained with human and non-human images. The output of the classifier was abounding box if there was any human in the image.
In the next, I used Kalman filter to track the detected human. To check the accuracy of my work, I created a ground truth based on the color tracker. You can read and download a similar one on my website here.
All text and images in this article are taken from my master thesis or respective publications, the full document can be downloaded here.
 N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886 –893 vol. 1, June 2005. doi: 10.1109/CVPR.2005.177.
 N. Dalal and B . Triggs. Histograms of oriented gradients for human detection., 2005.