# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 1 Introduction  
In recent years, the Internet of Things (IoT) and big data have grown substantially, and secure monitoring is one of the most challenging tasks [1; 2; 3]. According to data from HIS Markit, in 2017, 98 million new network monitoring cameras and 29 million high-definition (HD) closed-circuit television (CCTV) monitoring cameras were shipped globally through professional sales channels [4]. According to the \"In-Depth Research Report on Digital Surveillance Cameras in the Global and Chinese Markets in 2018\" published by Hangzhou Primus, the total sales volume of the global digital surveillance cameras market is expected to reach 19,312.19 million USD by 2022 [5]. With the increase of monitoring cameras, a large quantity of video data will be generated every day, which makes it impossible to manually process them. Thus, an automated and intelligent video processing system is urgently needed.  
Secure monitoring describes, interprets and predicts the behavior of moving targets by combining methods from computer vision [6; 7], artificial intelligence, image processing and pattern recognition. As an emerging technology, intelligent video monitoring faces many challenges due to rapid development and increasingly mature theoretical technologies. Video face tracking is an important technology for intelligent video monitoring. However, because of factors such as illumination variation, deformation, and occlusion, long-term tracking of arbitrary objects with high robustness and high real-time performance remains a challenging problem.  
In recent years, correlation filters and deep learning have been used for tracking and have achieved good results [8; 9]. The kernelized correlation filter [10] adopts a cyclic matrix to sample map ridgeregression in linear space to a multidimensional nonlinear space through the kernel function method and converts a large number of intermatrix operations into calculations in the Fourier domain, thus increasing the operation speed. Deep learning has achieved great success in detection and recognition. Some convolutional neural network (CNN)-based correlation filters tracking methods [11; 12] and deep learning detection methods [13; 14] also have high accuracy in tracking. However, a large number of samples with various types are required for training deep learning methods. Because of lacking data and the complexity of deep algorithms, deep learning is limited in tracking tasks. The main contributions of the paper are summarized as follows:  
1. A real-time face tracking framework that uses a tracking-by-detection method to combine tracking and detection is proposed.
2. The kernelized correlation filter is used to quickly track faces. A multitask cascade CNN, as a detector, is introduced to detect faces and modify the tracker's result. The cascade CNN detector ensures accuracy, and improves the speed by not performing frame-by-frame updating.
3. An update strategy is designed, in which detection result is used to correct the inaccurate tracking result, and the modified result is used to update the tracking filter model. Thus the proposed method can robustly track drastic occlusion and out-of-view objects.  
The experimental results show that the fast face tracking-by-detection (FFTD) algorithm has high robustness and real-time operation; therefore, high performance and long-term tracking secure monitoring can be achieved.  
The rest of this paper is organized as follows. Section 2 introduces related work on object tracking, face detection and tracking-by-detection methods. The pipeline of the proposed face tracking algorithm is introduced in Section 3. In Section 4, the experimental results of the proposed method are reported and discussed. Finally, Section 5 concludes the paper and presents future work.