High-efficiency face detection and tracking method for numerous pedestrians through face candidate generation

This paper is dedicated to developing high-efficiency face detection and tracking method for big dynamic crowds or numerous pedestrians. Three modules constitute the proposed method, i.e., face candidate generation, face candidate verification, and face target tracking. In this work, face candidates are localized using the features of the face area, edge information, and skin color. Non-face parts in the face candidates are further verified by the C-SVM learning model and then removed, by which the face targets can be generated with lower computation-complexity and satisfactory accuracy than other approaches. Finally, the face targets are tracked by an efficient and reliable searching scheme for improving the effective face detection rate. Experimental results show that the average face detection rate (FDR) of 85%, average effective FDR of 95%, a frame rate of 28-66 frames per second (fps), and about 30 faces detected per frame are obtained from various test videos with big dynamic crowds or numerous pedestrians, indicating the feasibility of the proposed method to achieve unconstrained face detection with high-efficiency and cost-effectiveness. This result makes the proposed method more attractive for the video surveillance system as compared to other approaches, especially in the high computational complexity-based methods.

Automated summary

This paper presents a high-efficiency face detection and tracking method for big dynamic crowds or numerous pedestrians, consisting of modules for face candidate generation, verification, and target tracking. Experimental results show an average face detection rate of 85%, average effective face detection rate of 95%, and a frame rate of 28-66 fps, with approximately 30 faces detected per frame, demonstrating the feasibility and cost-effectiveness of the proposed method for unconstrained face detection.