Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model

Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection

The Discriminative Correlation Filter (DCF) has been universally recognized in visual object tracking, thanks to its excellent accuracy and high speed. Nevertheless, these DCF-based trackers perform poorly in long-term tracking. The reasons include the following aspects—first, they have low adaptability to significant appearance changes in long-term tracking and are prone to tracking failure; second, these trackers lack a practical re-detection module to find the target again after tracking failure. In our work, we propose a new long-term tracking strategy to solve these issues. First, we make the best of the static and dynamic information of the target by introducing the motion features to our long-term tracker and obtain a more robust tracker. Second, we introduce a low-rank sparse dictionary learning method for re-detection. This re-detection module can exploit a correlation among these training samples and alleviate the impact of occlusion and noise. Third, we propose a new reliability evaluation method to model an adaptive update, which can switch expediently between the tracking module and the re-detection module. Massive experiments demonstrate that our proposed approach has an obvious improvement in precision and success rate over these state-of-the-art trackers.

Correlation Filter-Based Visual Tracking for UAV with Online Multi-Feature Learning

In this paper, a novel online learning-based tracker is presented for the unmanned aerial vehicle (UAV) in different types of tracking applications, such as pedestrian following, automotive chasing, and building inspection. The presented tracker uses novel features, i.e., intensity, color names, and saliency, to respectively represent both the tracking object and its background information in a background-aware correlation filter (BACF) framework instead of only using the histogram of oriented gradient (HOG) feature. In other words, four different voters, which combine the aforementioned four features with the BACF framework, are used to locate the object independently. After obtaining the response maps generated by aforementioned voters, a new strategy is proposed to fuse these response maps effectively. In the proposed response map fusion strategy, the peak-to-sidelobe ratio, which measures the peak strength of the response, is utilized to weight each response, thereby filtering the noise for each response and improving final fusion map. Eventually, the fused response map is used to accurately locate the object. Qualitative and quantitative experiments on 123 challenging UAV image sequences, i.e., UAV123, show that the novel tracking approach, i.e., OMFL tracker, performs favorably against 13 state-of-the-art trackers in terms of accuracy, robustness, and efficiency. In addition, the multi-feature learning approach is able to improve the object tracking performance compared to the tracking method with single-feature learning applied in literature.

Content-Aware Focal Plane Selection and Proposals for Object Tracking on Plenoptic Image Sequences

Object tracking is a fundamental problem in computer vision since it is required in many practical applications including video-based surveillance and autonomous vehicles. One of the most challenging scenarios in the problem is when the target object is partially or even fully occluded by other objects. In such cases, most of existing trackers can fail in their task while the object is invisible. Recently, a few techniques have been proposed to tackle the occlusion problem by performing the tracking on plenoptic image sequences. Although they have shown promising results based on the refocusing capability of plenoptic images, there is still room for improvement. In this paper, we propose a novel focus index selection algorithm to identify an optimal focal plane where the tracking should be performed. To determine an optimal focus index, we use a focus measure to find maximally focused plane and a visual similarity to capture the plane where the target object is visible, and its appearance is distinguishably clear. We further use the selected focus index to generate proposals. Since the optimal focus index allows us to estimate the distance between the camera and the target object, we can more accurately guess the scale changes of the object in the image plane. Our proposal algorithm also takes the trajectory of the target object into account. We extensively evaluate our proposed techniques on three plenoptic image sequences by comparing them against the prior tracking methods specialized to the plenoptic image sequences. In experiments, our method provides higher accuracy and robustness over the prior art, and those results confirm that the merits of our proposed algorithms.