Motion-Aware Correlation Filters for Online Visual Tracking

Abstract

The discriminative correlation filters-based methods struggle deal with the problem of fast motion and heavy occlusion, the problem can severely degrade the performance of trackers, ultimately leading to tracking failures. In this paper, a novel Motion-Aware Correlation Filters (MACF) framework is proposed for online visual object tracking, where a motion-aware strategy based on joint instantaneous motion estimation Kalman filters is integrated into the Discriminative Correlation Filters (DCFs). The proposed motion-aware strategy is used to predict the possible region and scale of the target in the current frame by utilizing the previous estimated 3D motion information. Obviously, this strategy can prevent model drift caused by fast motion. On the base of the predicted region and scale, the MACF detects the position and scale of the target by using the DCFs-based method in the current frame. Furthermore, an adaptive model updating strategy is proposed to address the problem of corrupted models caused by occlusions, where the learning rate is determined by the confidence of the response map. The extensive experiments on popular Object Tracking Benchmark OTB-100, OTB-50 and unmanned aerial vehicles (UAV) video have demonstrated that the proposed MACF tracker performs better than most of the state-of-the-art trackers and achieves a high real-time performance. In addition, the proposed approach can be integrated easily and flexibly into other visual tracking algorithms.

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

“…Yang et al [27] proposed an improved STC tracker to address occlusion through incorporating a Kalman filter for prediction of target location and uses Euclidean distance to detect occlusion. Zhang et al [28] proposed a motion aware correlation filter (MACF) which predicts position and scale of the target in the next frame by utilizing instantaneous motion estimation.…”

“…The CLE is defined as the Euclidean distance calculated by tracking algorithm and ground truth of target. The calculation formula is given in (28).…”

“…Eight image sequences are used in this experiment. We evaluate the proposed tracker in comparison with MOSSE CA and DCF CA [37], STC [21] and MACF [28] trackers both qualitatively and quantitatively.…”

Special Issue on Visual Sensors

“…Teng et al [2] provided a method for aircraft pose estimation without relying on 3D models using two widely separated cameras to acquire the pose information. In [3], a new framework for online visual object tracking is proposed. A motion-aware strategy is employed to predict the possible region and scale of the target in the frame by utilizing the previously estimated 3D motion information.…”

Robust Visual Tracking Using Structural Patch Response Map Fusion Based on Complementary Correlation Filter and Color Histogram

“…Additionally, Yang et al present parallel correlation filters (PCF) [22] for visual tracking by constructing two parallel correlation filters. Zhang et al propose a novel motion-aware correlation filters (MACF) tracking algorithm [23], which integrates instantaneous motion estimation Kalman filters into the correlation filters.…”