Robust Visual Detection–Learning–Tracking Framework for Autonomous Aerial Refueling of UAVs

Yin, Yingjie; Wang, Xingang; Xu, De; Liu, Fangfang; Wang, Yinglu; Wu, Wenqi

doi:10.1109/tim.2015.2509318

Cited by 71 publications

(23 citation statements)

References 38 publications

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“…Visual object tracking plays an important role for the unmanned aerial vehicle (UAV). In literature, it has been widely used in different types of UAV applications, such as person following [1], automobile chasing [2], see-and-avoid [3], infrastructure inspection [4], wildlife monitoring [5], autonomous landing [6], aerial manipulation [7], and air-to-air refuel [8]. Although a growing number of visual tracking approaches have been designed for the UAV recently [9][10][11][12][13][14][15][16][17], visual tracking is still a challenging issue because of object appearance changes, which are generated by object deformation, illumination variation, scale changes, partial or full occlusion, blur motion, fast motion, in-plane or out-of-plane rotation, low image resolution, and cluttered background.…”

Section: Introductionmentioning

confidence: 99%

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

Lin

et al. 2019

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

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

Lin

et al. 2019

Remote Sensing

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“…The proposed framework appropriately handled tracklet generation, progressive trajectory construction, and tracklet drifting and fragmentation, so that the unstable detection problem in aerial videos could be solved. Yin et al [20] proposed a dual-classifier-based tracking method in which the D-classifier used linear SVM to detect targets offline, and a T-classifier used state-based structured SVM to track targets online. Their combination can have an excellent tracking effect, but it cannot process in real time.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos

Yang

Bai

et al. 2019

Remote Sensing

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In recent years, UAV technology has developed rapidly. Due to the mobility, low cost, and variable monitoring altitude of UAVs, multiple-object detection and tracking in aerial videos has become a research hotspot in the field of computer vision. However, due to camera motion, small target size, target adhesion, and unpredictable target motion, it is still difficult to detect and track targets of interest in aerial videos, especially in the case of a low frame rate where the target position changes too much. In this paper, we propose a multiple-object-tracking algorithm based on dense-trajectory voting in aerial videos. The method models the multiple-target-tracking problem as a voting problem of the dense-optical-flow trajectory to the target ID, which can be applied to aerial-surveillance scenes and is robust to low-frame-rate videos. More specifically, we first built an aerial video dataset for vehicle targets, including a training dataset and a diverse test dataset. Based on this, we trained the neural network model by using a deep-learning method to detect vehicles in aerial videos. Thereafter, we calculated the dense optical flow in adjacent frames, and generated effective dense-optical-flow trajectories in each detection bounding box at the current time. When target IDs of optical-flow trajectories are known, the voting results of the optical-flow trajectories in each detection bounding box are counted. Finally, similarity between detection objects in adjacent frames was measured based on the voting results, and tracking results were obtained by data association. In order to evaluate the performance of this algorithm, we conducted experiments on self-built test datasets. A large number of experimental results showed that the proposed algorithm could obtain good target-tracking results in various complex scenarios, and performance was still robust at a low frame rate by changing the video frame rate. In addition, we carried out qualitative and quantitative comparison experiments between the algorithm and three state-of-the-art tracking algorithms, which further proved that this algorithm could not only obtain good tracking results in aerial videos with a normal frame rate, but also had excellent performance under low-frame-rate conditions.

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“…The main problem of generative trackers is that the appearance model often exhibits some limitations thus cannot represent the target effectively [7][8][9]. Hybrid generative-discriminative trackers fuse the advantages of discriminative and generative trackers; researchers have proposed many effective hybrid generative-discriminative trackers recently [10][11][12][13]. The hybrid model can take advantage of the global characteristics of object and also exploit the useful information from the background.…”

Section: Introductionmentioning

confidence: 99%

Robust L1 tracker with CNN features

Wang

2017

J Wireless Com Network

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Recently, L1 tracker has been widely applied and received great success in visual tracking. However, most L1 trackers use only the image intensity for sparse representation, which is insufficient to represent the object especially when drastic appearance changes occur. Convolutional neural network (CNN) has demonstrated remarkable capability in a wide range of computer vision fields, and features extracted from different convolutional layers have different characteristics. In this paper, we propose a novel sparse representation model with convolutional features for visual tracking. Besides, to alleviate the redundancy from high-dimensional convolutional features, a feature selection method is adopted to remove noisy and irrelevant feature maps, which can reduce computation redundancy and improve tracking accuracy. Different from traditional sparse representation based tracking methods, our model not only exploits convolutional features to improve the robustness for describing the object appearance but also uses the trivial templates to model both reconstruction errors caused by sparse representation and the eigensubspace representation. In addition, an unified objective function is proposed and a customized APG method is developed to effectively solve the optimization problem. Numerous qualitative and quantitative evaluations demonstrate that our tracker outperforms other state-of-the-art trackers in a wide range of tracking scenarios.

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Robust Visual Detection–Learning–Tracking Framework for Autonomous Aerial Refueling of UAVs

Cited by 71 publications

References 38 publications

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

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

Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos

Robust L1 tracker with CNN features

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