HOG-PCA descriptor with optical flow based human detection and tracking

TLDR: The main contribution of this paper is to reduce the computational time of HOG by dynamically determining the region of interest and limiting the scan area and not only reduces detection time but also reduces the number of false positives and increases the efficiency.

Abstract: Human detection and tracking is an interesting field of research in computer vision and image processing areas. It is widely used in video surveillance, robotics, human machine interaction and other applications. The automated object detection and tracking is still a challenging task that needs to be addressed. Hence the main idea is to develop a system based on various image processing techniques to reliably detect and track people in video sequence from stationary cameras. The proposed method can be viewed as consisting of two stages namely detection and tracking. In detection stage, Histogram of Oriented Gradients popularly known as HOG is used as a feature descriptor. HOG features are robust to local changes in geometry and illumination but it is computationally expensive. This disadvantage of HOG is due to its exhaustive scanning approach over entire region of interest. The main contribution of this paper is to reduce the computational time of HOG by dynamically determining the region of interest and limiting the scan area. Further Principal component analysis is used to reduce the dimensionality of HOG features. The additional use of optical flow based tracking eliminates the need of HOG computation in every frame. Hence the person detected by HOG in initial frame is successfully tracked in subsequent frames and reduces HOG computation time. The proposed dynamic ROI selection method not only reduces detection time but also reduces the number of false positives and increases the efficiency. The experimental results show that the system efficiently detects and tracks people in videos without much of occlusion.