Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation

Aerial tracking, which has exhibited its omnipresent dedication and splendid performance, is one of the most active applications in the remote sensing field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system, equipped with a visual tracking approach, has been widely used in aviation, navigation, agriculture,transportation, and public security, etc. As is mentioned above, the UAV-based aerial tracking platform has been gradually developed from research to practical application stage, reaching one of the main aerial remote sensing technologies in the future. However, due to the real-world onerous situations, e.g., harsh external challenges, the vibration of the UAV mechanical structure (especially under strong wind conditions), the maneuvering flight in complex environment, and the limited computation resources onboard, accuracy, robustness, and high efficiency are all crucial for the onboard tracking methods. Recently, the discriminative correlation filter (DCF)-based trackers have stood out for their high computational efficiency and appealing robustness on a single CPU, and have flourished in the UAV visual tracking community. In this work, the basic framework of the DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art DCF-based trackers are orderly summarized according to their innovations for solving various issues. Besides, exhaustive and quantitative experiments have been extended on various prevailing UAV tracking benchmarks, i.e., UAV123, UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903 frames in total. The experiments show the performance, verify the feasibility, and demonstrate the current challenges of DCF-based trackers onboard UAV tracking.Comment: 28 pages, 10 figures, submitted to GRS

Surrounding-aware correlation filter for UAV tracking with selective spatial regularization

The great advance of visual object tracking has provided unmanned aerial vehicle (UAV) with intriguing capability for various practical applications. With promising performance and efficiency, discriminative correlation filter-based trackers have drawn great attention and undergone remarkable progress. However, background interference and boundary effect remain two thorny problems. In this paper, a surrounding-aware tracker with selective spatial regularization (SASR) is presented. SASR tracker extracts surrounding samples according to the size and shape of the object in order to utilize context and maintain the integrality of the object. Additionally, a selective spatial regularizer is introduced to address boundary effect. Central coefficients in the filter are evenly regularized to preserve valid information from the object. While the others are penalized according to their spatial location. Under the framework of SASR tracker, surrounding information and selective spatial regularization prove to be complementary to each other, which actually did not draw much attention before. They managed to improve not only the robustness against various distractions in the surrounding but also the flexibility to catch up with frequent appearance change of the object. Qualitative evaluation and quantitative experiments on challenging UAV tracking sequences have shown that SASR tracker has performed favorably against 23 state-of-the-art trackers.The work was supported by the National Natural Science Fundation of China (no. 61806148) and the Fundamental Research Funds for the Central Universities (no.22120180009)

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

Learning Temporary Block-Based Bidirectional Incongruity-Aware Correlation Filters for Efficient UAV Object Tracking

In the field of UAV object tracking, correlation filter based approaches have received lots of attention due to their computational efficiency. The methods learn filters by the ridge regression and generate response maps to distinguish the specified target from the background. An ideal filter can predict the object’s position in a new frame, and in turn, can backtrack the object in the past frames. However, the neglect of tracking reversibility in most methods limits the potential of using inter-frame information to improve performance. In this work, a novel bidirectional incongruity-aware correlation filter is presented based on the nature of tracking reversibility. The proposed method incorporates the response-based bidirectional incongruity, which represents the gap between the filters’ discriminative difference in the forward and backward tracking perspective caused by object appearance changes. It enables the filter not only to inherit the discriminability from previous filters but also to enhance the generalization capability to unpredictable appearance variations in upcoming frames. Moreover, a temporary block-based strategy is introduced to empower the filter accommodate more drastic object appearance changes and make more effective use of inter-frame information. Comprehensive experiments are conducted on three challenging UAV tracking benchmarks, including UAV123@10fps, DTB70, and UAVDT. Experimental results indicate that the proposed method has superior performance compared with the other 34 state-of-the-art trackers. Our approach permits real-time performance at ~46.8 FPS on a single CPU and is suitable for UAV online tracking applications

Anti-cancer activity of Tonglian decoction against esophageal cancer cell proliferation through regulation of the cell cycle and PI3K/Akt signaling pathway

Objective: The purpose of this study was to observe the anti-cancer activity of Tonglian decoction (TD) on esophageal cancer (EC) cells in vitro, and to elucidate the related molecular mechanisms in the cell cycle and PI3K/Akt signaling pathway. Methods: EC9706 cells were cultured in RPMI 1640 medium supplemented with 10% calf serum at 37°C in a 5% CO2 incubator. The cells were treated with rat serum containing TD or the serum of rats administered Xiaoaiping as a positive control drug. Cell proliferation was assessed by methylthiazolyldiphenyl-tetrazolium bromide assays. Cell morphology was observed under a microscope. The cell cycle was examined by flow cytometry. Protein expression in the PI3K/Akt signaling pathway was measured by western blotting. Results: TD mainly inhibited cell proliferation. Concentrations of 50% cell inhibition by rat serum containing TD or Xiaoaiping were 73.6 and 153.8 μL/mL, respectively. TD also influenced cell morphology characterized by small shrunken cells. Cell colonies became small and the cell proliferation rate was slower. In cell cycle analysis, the percentage of cells in S phase was decreased significantly by TD and Xiaoaiping compared with the blank control group (P < .05). Western blotting showed that serum containing TD strongly down-regulated EGFR, PI3K, Akt, p-Akt, and mTOR expression compared with the blank control group (P < .05). Conclusion: TD could inhibit EC9706 carcinoma cell proliferation by blocking the cell cycle progression in S phase. The possible mechanism was inhibition of multiple targets in the PI3K/Akt signaling pathway by TD