Unmanned aerial vehicle video-based target tracking algorithm Using sparse representation

Target tracking based on unmanned aerial vehicle (UAV) video is a significant technique in intelligent urban surveillance systems for smart city applications, such as smart transportation, road traffic monitoring, inspection of stolen vehicle, etc. In this paper, a vision-based target tracking algorithm aiming at locating UAV-captured targets, like pedestrian and vehicle, is proposed using sparse representation theory. First of all, each target candidate is sparsely represented in the subspace spanned by a joint dictionary. Then, the sparse representation coefficient is further constrained by an L2 regularization based on the temporal consistency. To cope with the partial occlusion appearing in UAV videos, a Markov Random Field (MRF)-based binary support vector with contiguous occlusion constraint is introduced to our sparse representation model. For long-term tracking, the particle filter framework along with a dynamic template update scheme is designed. Both qualitative and quantitative experiments implemented on visible (Vis) and infrared (IR) UAV videos prove that the presented tracker can achieve better performances in terms of precision rate and success rate when compared with other state-of-the-art tracker

Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering

Recently, Vision Language Models (VLMs) have gained significant attention, exhibiting notable advancements across various tasks by leveraging extensive image-text paired data. However, prevailing VLMs often treat Visual Question Answering (VQA) as perception tasks, employing black-box models that overlook explicit modeling of relationships between different questions within the same visual scene. Moreover, the existing VQA methods that rely on Knowledge Bases (KBs) might frequently encounter biases from limited data and face challenges in relevant information indexing. Attempt to overcome these limitations, this paper introduces an explainable multi-agent collaboration framework by tapping into knowledge embedded in Large Language Models (LLMs) trained on extensive corpora. Inspired by human cognition, our framework uncovers latent information within the given question by employing three agents, i.e., Seeker, Responder, and Integrator, to perform a top-down reasoning process. The Seeker agent generates relevant issues related to the original question. The Responder agent, based on VLM, handles simple VQA tasks and provides candidate answers. The Integrator agent combines information from the Seeker agent and the Responder agent to produce the final VQA answer. Through the above collaboration mechanism, our framework explicitly constructs a multi-view knowledge base for a specific image scene, reasoning answers in a top-down processing manner. We extensively evaluate our method on diverse VQA datasets and VLMs, demonstrating its broad applicability and interpretability with comprehensive experimental results.Comment: 16 pages, 9 figure

Infrared image enhancement using adaptive histogram partition and brightness correction

Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods

Visual Target Tracking using Robust Information Interaction between Single Tracker and Online Model

In this paper, a novel tracking algorithm based on the cooperative operation of online appearance model and typical tracking in contiguous frames is proposed. First of all, to achieve satisfactory performances in challenging scenes, we focus on establishing a robust discriminative tracking model with linear Support Vector Machine (SVM) and use the particle filter for localization. Intended to fit the particle filter, the outputs of SVM classifier are mapped into probabilities with a sigmoid function so that the posterior of candidate samples is estimated. Then, the tracking loop starts with median flow method and the coordinated operation of the two trackers is mediated by the maximum a posteriori (MAP) estimate for the target probability of negative samples, which is defined during the sigmoid fit. Lastly, for the purpose of model update, we sum up the optimal SVM using a prototype set with the predefined budget, and the classifier is updated on both the prototype set and the updated data from the tracking results every few frames. A number of comparative experiments are conducted on real video sequences and both qualitative and quantitative evaluations demonstrate a robust and precise performance of our method

Visual Target Tracking using Robust Information Interaction between Single Tracker and Online Model

In this paper, a novel tracking algorithm based on the cooperative operation of online appearance model and typical tracking in contiguous frames is proposed. First of all, to achieve satisfactory performances in challenging scenes, we focus on establishing a robust discriminative tracking model with linear Support Vector Machine (SVM) and use the particle filter for localization. Intended to fit the particle filter, the outputs of SVM classifier are mapped into probabilities with a sigmoid function so that the posterior of candidate samples is estimated. Then, the tracking loop starts with median flow method and the coordinated operation of the two trackers is mediated by the maximum a posteriori (MAP) estimate for the target probability of negative samples, which is defined during the sigmoid fit. Lastly, for the purpose of model update, we sum up the optimal SVM using a prototype set with the predefined budget, and the classifier is updated on both the prototype set and the updated data from the tracking results every few frames. A number of comparative experiments are conducted on real video sequences and both qualitative and quantitative evaluations demonstrate a robust and precise performance of our method

Regulation of DNA‐PK activity promotes the progression of TNBC via enhancing the immunosuppressive function of myeloid‐derived suppressor cells

Abstract Background DNA‐dependent protein kinase (DNA‐PK) is engaged in DNA damage repair and is significantly expressed in triple negative breast cancer (TNBC). Inhibiting DNA‐PK to reduce DNA damage repair provides a possibility of tumor treatment. NU7441, a DNA‐PK inhibitor, can regulate the function and differentiation of CD4+T cells and effectively enhance immunogenicity of monocyte‐derived dendritic cells. However, the effect of NU7441 on the tumor progression activity of immunosuppressive myeloid‐derived suppressor cells (MDSCs) in TNBC remains unclear. Results In this study, we found that NU7441 alone significantly increased tumor growth in 4 T1 (a mouse TNBC cell line) tumor‐bearing mice. Bioinformatics analysis showed that DNA‐PK and functional markers of MDSCs (iNOS, Arg1, and IDO) tended to coexist in breast cancer patients. The mutations of these genes were significantly correlated with lower survival in breast cancer patients. Moreover, NU7441 significantly decreased the percentage of MDSCs in peripheral blood mononuclear cells (PBMCs), spleen and tumor, but enhanced the immunosuppressive function of splenic MDSCs. Furthermore, NU7441 increased MDSCs' DNA‐PK and pDNA‐PK protein levels in PBMCs and in the spleen and increased DNA‐PK mRNA expression and expression of MDSCs functional markers in splenic MDSCs from tumor‐bearing mice. NU7441 combined with gemcitabine reduced tumor volume, which may be because gemcitabine eliminated the remaining MDSCs with enhanced immunosuppressive ability. Conclusions These findings highlight that the regulation of DNA‐PK activity by NU7441 promotes TNBC progression via enhancing the immunosuppressive function of MDSCs. Moreover, NU7441 combined with gemcitabine offers an efficient therapeutic approach for TNBC and merits deeper investigation

Fault-tolerant FADS system development for a hypersonic vehicle via neural network algorithms

Hypersonic vehicles suffer from extreme aerodynamic heating during flights, especially around the area of leading edge due to its small curvature. Therefore, flush air data sensing (FADS) system has been developed to perform accurate measurement of the air data parameters. In the present study, the method to develop the FADS algorithms with fail-operational capability for a sharp-nosed hypersonic vehicle is provided. To be specific, the FADS system implemented with 16 airframe-integrated pressure ports is used as a case study. Numerical simulations of different freestream conditions have been conducted to generate the database for the FADS targeting in 2≤Ma≤5 and 0km≤H≤30km. Four groups of neural network algorithms have been developed based on four different pressure port configurations, and the accuracy has been validated by 280 groups of simulations. Particularly, the algorithms based on the 16-port configuration show an excellent ability to serve as the main solver of the FADS, where 99.5% of the angle-of-attack estimations are within the error band ±0.2∘. The accuracy of the algorithms is discussed in terms of port configuration. Furthermore, diagnosis of the system health is present in the paper. A fault-tolerant FADS system architecture has been designed, which is capable of continuously sensing the air data in the case that multi-port failure occurs, with a reduction in the system accuracy

Inhibition of O-linked N-acetylglucosamine transferase activity in PC12 cells - A molecular mechanism of organophosphate flame retardants developmental neurotoxicity

Organophosphate flame retardants (OPFRs), as alternatives of brominated flame retardants, can cause neuro-developmental effects similar to organophosphate pesticides. However, the molecular mechanisms underlying the toxicity remain elusive. O-linked N-acetylglucosamine (O-GlcNAc) transferase (OGT) regulates numerous neural processes through the O-GlcNAcylation modification of nuclear and cytoplasmic proteins. In this study, we aimed to investigate the molecular mechanisms accounting for the developmental neurotoxicity of OPFRs by identifying potential targets of OPFRs and the attendant effects. Twelve OPFRs were evaluated for inhibition of OGT activity using an electrochemical biosensor. Their potency differed with substituent groups. The alkyl group substituted OPFRs had no inhibitory effect. Instead, the six OPFRs substituted with aromatic or chlorinated alkyl groups inhibited OGT activity significantly, with tri-m-cresyl phosphate (TCrP) being the strongest. The six OPFRs (0-100 mu M exposure) also inhibited OGT activity in PC12 cells and decreased protein O-GlcNAcylation level. Inhibition of OGT by OPFRs might be involved in the subsequent toxic effects, including intracellular reactive oxygen species (ROS), calcium level, as well as cell proliferation and autophagy. Molecular docking of the OGT/OPFR complexes provided rationales for the difference in their structure-dependent inhibition potency. Our findings may provide a new biological target of OPFRs in their neurotoxicological actions, which might be a major molecular mechanism of OPFRs developmental neurotoxicity