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Numerous improvements in feedback mechanisms have contributed to the great progress in object detection. In this paper, we first present an evaluation-feedback module, which consists of an evaluation system and feedback mechanism. Then we analyze and summarize traditional evaluation-feedback modules. We focus on both the evaluation system and the feedback mechanism, and propose C ontrol D istance IoU and C ontrol D istance IoU loss function (CDIoU and CDIoU loss) without increasing parameters in models, which make significant enhancements on several classical and emerging models. Finally, we propose A utomatic G round T ruth C lustering (AGTC) and F loating L earning R ate D ecay (FLRD) for faster regression in object detection. Experiments show that a coordinated evaluation-feedback module can effectively improve model performance. Both CNN and transformer-based detectors with CDIoU + CDIoU loss, AGTC, and FLRD achieve excellent performances. There are a maximum AP improvement of 2.9%, an average AP of 1.1% improvement on MS COCO, a maximum AP improvement of 8.2%, and an average AP improvement of 3.7% on Visdrone dataset. [ABSTRACT FROM AUTHOR]

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find noncontiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. [ABSTRACT FROM AUTHOR]

Quality of clustering is an important issue in application of clustering techniques. Most traditional cluster validity indices are geometry-based cluster quality measures. This paper proposes a cluster validity index based on the decision-theoretic rough set model by considering various loss functions. Experiments with synthetic, standard, and real-world retail data show the usefulness of the proposed validity index for the evaluation of rough and crisp clustering. The measure is shown to help determine optimal number of clusters, as well as an important parameter called threshold in rough clustering. The experiments with a promotional campaign for the retail data illustrate the ability of the proposed measure to incorporate financial considerations in evaluating quality of a clustering scheme. This ability to deal with monetary values distinguishes the proposed decision-theoretic measure from other distance-based measures. The proposed validity index can also be extended for evaluating other clustering algorithms such as fuzzy clustering. [ABSTRACT FROM AUTHOR]

Intelligent video surveillance network has many practical applications such as human tracking, vehicle tracking, and event detection. In this paper, an activemulticamera network framework is designed for human detection and tracking by optimizing the cameras collaborating control. A multicamera collaborating control algorithm is proposed based on Bayes network to minimize the number of PTZ cameras with control and optimize the cameras' field of view. Hybrid human local feature transform selected by AdaBoost algorithm is adopted to improve the tracking precision. Experimental results on real world environment indicate the effectiveness and efficiency of proposed framework and algorithm. [ABSTRACT FROM AUTHOR]

Ridge regression (RR) and its extended versions are widely used as an effective feature extraction method in pattern recognition. However, the RR-based methods are sensitive to the variations of data and can learn only limited number of projections for feature extraction and recognition. To address these problems, we propose a new method called robust discriminant regression (RDR) for feature extraction. In order to enhance the robustness, the L 2,1 -norm is used as the basic metric in the proposed RDR. The designed robust objective function in regression form can be solved by an iterative algorithm containing an eigenfunction, through which the optimal orthogonal projections of RDR can be obtained by eigen decomposition. The convergence analysis and computational complexity are presented. In addition, we also explore the intrinsic connections and differences between the RDR and some previous methods. Experiments on some well-known databases show that RDR is superior to the classical and very recent proposed methods reported in the literature, no matter the L 2 -norm or the L 2,1 -norm-based regression methods. The code of this paper can be downloaded from http://www.scholat.com/laizhihui.