Correlation filters (CFs) have been continuously advancing the state-of-the-art tracking performance and have been extensively studied in the recent few years. Most of the existing CF trackers adopt a cosine window to spatially reweight base image to alleviate boundary discontinuity. However, cosine window emphasizes more on the central region of base image and has the risk of contaminating negative training samples during model learning. On the other hand, spatial regularization deployed in many recent CF trackers plays a similar role as cosine window by enforcing spatial penalty on CF coefficients. Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization. When simply removing cosine window, CF with spatial regularization still suffers from small degree of boundary discontinuity. To tackle this issue, binary and Gaussian shaped mask functions are further introduced for eliminating boundary discontinuity while reweighting the estimation error of each training sample, and can be incorporated with multiple CF trackers with spatial regularization. In comparison to the counterparts with cosine window, our methods are effective in handling boundary discontinuity and sample contamination, thereby benefiting tracking performance. Extensive experiments on three benchmarks show that our methods perform favorably against the state-of-the-art trackers using either handcrafted or deep CNN features. The code is publicly available at this https URL.
The core problem of CF trackers is to learn a discriminative filter for the next frame from current frame and historical information. Early methods, e.g., MOSSE @cite_20 and KCF @cite_43 , formulate the CF framework with one single base image from the current frame, and update the CFs using the linear interpolation strategy. Denote by the sample pair @math in frame @math , where each sample @math consists of @math feature maps with @math , and @math represents the Gaussian shaped label. Then the correlation filter @math is obtained by minimizing the following objective, where @math and @math respectively stand for circular convolution and Hadamard product, @math denotes cosine window, and @math denotes the tradeoff parameter of the regularization term @math .
@cite_43 @cite_20
2798770428 2154889144 2317058546 2964198573
For visual tracking, an ideal filter learned by the correlation filter (CF) method should take both discrimination and reliability information. However, existing attempts usually focus on the former one while pay less attention to reliability learning. This may make the learned filter be dominated by the unexpected salient regions on the feature map, thereby resulting in model degradation. To address this issue, we propose a novel CF-based optimization problem to jointly model the discrimination and reliability information. First, we treat the filter as the element-wise product of a base filter and a reliability term. The base filter is aimed to learn the discrimination information between the target and backgrounds, and the reliability term encourages the final filter to focus on more reliable regions. Second, we introduce a local response consistency regular term to emphasize equal contributions of different regions and avoid the tracker being dominated by unreliable regions. The proposed optimization problem can be solved using the alternating direction method and speeded up in the Fourier domain. We conduct extensive experiments on the OTB-2013, OTB-2015 and VOT-2016 datasets to evaluate the proposed tracker. Experimental results show that our tracker performs favorably against other state-of-the-art trackers. The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies—any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source. Among increasingly complicated trackers in visual tracking area, recently proposed correlation filter based trackers have achieved appealing performance despite their great simplicity and superior speed. However, the filter input is a bounding box of fixed size, so they are not born with the adaptability to target’s scale and aspect ratio changes. Although scaleadaptive variants have been proposed, they are not flexible enough due to pre-defined scale sampling manners. Moreover, to the best of our knowledge, no correlation filter variant has been proposed to handle aspect ratio variation. To tackle this problem, this paper integrates the class-agnostic detection proposal method, which is widely adopted in object detection area, into a correlation filter tracker, and presents KCFDP tracker. The correlation filter part of KCFDP is based on KCF[2] with some modifications. We extend the HOG feature in KCF to a combination of HOG, intensity, and color naming by simply concatenating the three features, resulting in 42 feature channels. The model updating scheme in KCF, which is simple linear interpolation, is substituted with a more robust scheme presented in [1]. EdgeBoxes[4] is adopted to generate flexible detection proposals and enable the scale and aspect ratio adaptability of our tracker. It traverses the whole image in a sliding window manner, and scores every sampled bounding box according to the number of contours that are wholly enclosed. To accelerate EdgeBoxes and produce less unnecessary proposals, we set the minimum proposal area and aspect ratio range dynamically in sliding window sampling according to the current target size. In the tracking pipeline, KCF is firstly performed to estimate the preliminary target location ld . Within a patch zd extracted from current frame, KCF locates the target center according to the location of the maximum element in f : f(zd) = kxz d · α, (1) Discriminative correlation filters (DCF) with deep convolutional features have achieved favorable performance in recent tracking benchmarks. However, most of existing DCF trackers only consider appearance features of current frame, and hardly benefit from motion and inter-frame information. The lack of temporal information degrades the tracking performance during challenges such as partial occlusion and deformation. In this paper, we propose the FlowTrack, which focuses on making use of the rich flow information in consecutive frames to improve the feature representation and the tracking accuracy. The FlowTrack formulates individual components, including optical flow estimation, feature extraction, aggregation and correlation filters tracking as special layers in network. To the best of our knowledge, this is the first work to jointly train flow and tracking task in deep learning framework. Then the historical feature maps at predefined intervals are warped and aggregated with current ones by the guiding of flow. For adaptive aggregation, we propose a novel spatial-temporal attention mechanism. In experiments, the proposed method achieves leading performance on OTB2013, OTB2015, VOT2015 and VOT2016.