Object Detection and Tracking detects the people available in a video frame and tracks them in subsequent frames. In this project, there are two major parts: Detection and Tracking. The detection of human shape in a video frame was detected after Histogram of Oriented Gradients (HOG) features were calculated and was classified with using Linear Support Vector Machine (SVM). The testing was done in MIT and INRIA pedestrian dataset where the MIT dataset had 509 training and 200 test images of pedestrians in city scenes. The INRIA dataset had 1805 photos with 64x128 resolution cropped from a large set of personal photos. For each image, the histogram of Oriented Gradients was calculated and the classification was done. The detection accuracy of the application was 85% based on the MIT and INRIA Person Detection Dataset. The detected human was tracked using Kalman Filter.