# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 2 Related Work
Moreover, with both the proposal of light networks, such as MobileNet [36], and the advent of low-cost hardware with GPUs and embedded segments, e.g., NVIDIA Jetson TX2 and Google Coral, deep learning based algorithms can increasingly be applied to smaller devices such as drones, robots, and medical devices. As a trade-off between accuracy and efficiency, correlation filter-based algorithms have attracted extensive attention in recent years. [PERSON] et al. proposed a new type of correlation filter for visual tracking, named Minimum Output Sum of Squared Error (MOSSE) [18]. Since MOSSE only uses a single-channel grayscale feature, its practical performance is not desirable. However, the speed is over 600 frames per second (FPS), prompting researchers to discover the potential value of correlation filters. [PERSON] et al. developed the circulant structure of tracking-by-detection with kernels (CSK) [19], which adopted the well-established theory of circulant matrices based on MOSSE. The resulting tracker increases the number of training samples, thus improves the tracking accuracy. [PERSON] et al. proposed a high-speed tracker which trains a discriminant classifier using Kernelized Correlation Filters (KCF) [20]. The derived tracker extended the single-channel grayscale feature to the multi-channel histogram of oriented gradient (HOG) [5] feature and maintained high-speed capability. Aiming at scale adaption, Discriminative Scale Space Tracking (DSST) [21] learned two separated discriminative correlation filters for translation and scale estimation. Since the circulant matrix is introduced for sampling in the correlation filters, the cosine window is applied to ensure the rationality of samples. Nevertheless, the cosine window would lead to the boundary effect [9] which weakens the performance of trackers on fast motion targets. To solve that problem, [PERSON] et al. added spatial regularization to the discriminative correlation filters (SRDCF) [23]. Specifically, this penalizes correlation filter coefficients near the boundary and enlarges the searching area to learn more negative samples. One obvious shortcoming is that it destroys the closed form of discriminative correlation filters (DCF); instead, it uses Gauss-Seidel iterative optimization to obtain the optimal solution. As a result, it increases tracking quality at the expense of running speed. [PERSON] et al. [24] proposed a long-term tracker which learns both the long-term and short-term memory of target appearance. For preventing severe tracking failures, a learned detector is used to recover the target position. Recently, [PERSON] et al. [25] applied adaptive context awareness and trained a structural correlation filter for tracking, to make the tracker learn more context information. [PERSON] et al. [26] proposed a failure detection and re-tracking algorithm based on KCF performed in real-time. [PERSON] et al. [27] trained a large margin correlation filter to maximize the margin between the tracking target and its background. In addition, the correlation filter was trained with multi-level scale supervision to make it sensitive to scale variation. In this paper, we propose a tracker which follows the state-of-the-art algorithm Staple mentioned above. The proposed tracker uses more comprehensive features to describe the target, as well as an adaptive model update strategy to avoid model drift to make the tracker more robust.