## 1 Introduction
[28] used various fusion rules to combine different features for better description. For correlation filters, boundary effects might lead to detection failure; hence, [PERSON] et al. [29] (SRDCF) added a spatial regularization term to penalize the CF coefficients around the boundary. The SRDCF yields excellent tracking results; however, the real-time performance is degraded enormously, with a reported speed of only 5 FPS. In the development of trackers that are based on CF, the discrimination performance should be improved and the real-time performance requirement should be satisfied. Due to their strong feature representation performances, CNNs have realized significant success on visual tracking tasks and in many other scenarios. Many recent trackers [18; 19; 20; 21; 30; 31; 32] have demonstrated high-performance on benchmarks. [PERSON] et al. [32] and [PERSON] et al. [31] proposed the best-performing solutions for the visual object test (VOT) [33] long-term tracking and short-term tracking, respectively. [PERSON] et al. [30] proposed a method for enhancing the precision and robustness by learning the features that are extracted from deep convolutional neural networks and a CNN that is trained on object recognition datasets. [PERSON] et al. [21] exploited an implicit interpolation model with the objective of solving the learning problem in the continuous spatial domain and proposed an innovative method for efficiently combining multi-resolution feature maps. [PERSON] et al. [20] exploited a network by incorporating domain-specific layers and shared layers to obtain generic target representations. [PERSON] et al. [34] proposed a generative system that is based on CNN, which has realized satisfactory performance. [PERSON] et al. [35] proposed a novel hierarchical feature learning framework and [PERSON] et al. [36] revisited the standard SRDCF formulation and introduced padless correlation filters (PCFs), which could completely remove boundary effects. The studies that are discussed above have demonstrated the high power of CNN for target representation at the expense of high computational complexity and time consumption. In recent years, the practice of evaluating tracking algorithms has substantially improved. In the past, researchers were limited to evaluating the tracking performance of a small number of sequences [37; 38]. Benchmarks such as VOT, and the object tracking benchmark (OTB) [39; 40] emphasize the importance of test methods for a wider range of sequence sets that cover a variety of object categories and challenges. OTB contains 25% grayscale sequences, while VOT contains only colour sequences. OTB includes the start of a random frame and is initialized via the addition of random interference, whereas VOT is initialized and run from the first frame. In this paper, an understandable and efficient method is proposed for solving the problem that is described above. The main contributions can be summarized as follows: Two image representations of template and colour characteristics are combined to address illumination changes and shape variations. The discriminative CF is exploited based on a scale pyramid representation to solve the scale estimation problem. A high-confidence judgement mechanism is explored for avoiding model corruption. Figure 1 shows the flow of the target tracking algorithm in this paper.