# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 1 Introduction  
Visual object tracking is an important branch of computer vision, which can be described as the process of estimating the motion trajectory of a target in subsequent frames, provided that the state information (position, size, etc.) of the target is given in the first frame of the tracking sequence [1; 2; 3; 4]. The traditional visual object tracking algorithms mainly include Mean Shift [5], Lucas Kanade [6], Particle filter [7], etc. In 2010, [PERSON] [8] introduced correlation filtering into the field of object tracking and proposed the MOSSE (Minimum Output Sum of Squared Error) algorithm. The essence of MOSSE is to first construct a correlation filter with the features of the target in the first video frame; then, starting from the second video frame, the filter correlates the features of the target by Fast Fourier Transform (FFT), which is the reason why the tracking speed is so fast. The maximum value of the filter response is the predicted position of the target, and the filter is updated with the predicted position of the target. Based on MOSSE, many kinds of variant algorithms have been proposed, such as Circulant Structure of Tracking-by-Detection with Kernels (CSK) [9], Kernelized Correlation Filters (KCF) [10], Scale Adaptive Multiple Feature (SAMF) [11], to further enhance the MOSSE's performance. However, most of these variant algorithms extract target features by hand, which lacks flexibility, online performance, and accuracy for feature extraction. Therefore, it is necessary for the tracking based on the correlation filter to have some ways to obtain target features online with high flexibility and accuracy. CNNs (convolutional neural networks) have been widely studied in the field of image recognition since the introduction of a new deep structure and dropout method by [PERSON] by [PERSON] et al. [12]. Along with its rapid development, CNN has become one of the main techniques [13] in the field of feature extraction because of its strong feature representation ability. Therefore, a deep convolutional neural network combined with correlation filter tracking [8; 9; 10; 11; 14] (named Deep Correlation Filter Tracking) is gradually becoming one of the critical issues in the field of visual object tracking [1; 2; 3; 4; 15]. Firstly, deep correlation filter tracking takes a deep convolutional neural network as the feature extractor to online extract deep features from video frames by making full use of the powerful feature representation capabilities of CNN; and secondly, the correlation filter is applied to correlate the target features obtained from CNN to predict target positions with high rapidity. The general process of the deep correlation filter tracking model is shown in Figure 1.  
Despite many advances in deep correlation filter tracking models and their successful application in related fields, there are still some challenging problems that need to be addressed. For example, (1) deep convolutional neural networks have powerful feature representation capabilities that improve tracking accuracy. However, their large model size, high computational complexity, and high memory resource requirements increase the complexity of the model, as well as the computational burden, which makes it difficult to meet the real-time requirements of object tracking and also hinders its deployment in embedded mobile devices with limited computational and memory resources [16]. (2) In the actual tracking process, the deep convolutional neural network obtained by offline training is difficult to adapt to the changes of environment and target (e.g., changes in illumination, changes in target scale, rotation, background interference, etc. [17]), which restrains the accuracy of target feature extraction and may lead to lower tracking accuracy, thus affecting the tracking performance.