International Journal of Pattern Recognition and Artificial Intelligence

In this study, a novel hybrid deep clustering approach is proposed for the effective moving object segmentation. Initially, the data is collected, and the keyframe selection is performed using the threshold-based Kennard–Stone method. Then, the preprocessing step involves noise filtering using bilateral wavelet thresholding and binary color conversion. The blob detection is performed using normalized Laplacian of Gaussian. Finally, the segmentation of moving objects is performed using a hybrid clustering approach called improved fuzzy C-mean (IFCM) clustering with chaotic salp swarm (CSS) optimization algorithm (Hybrid IFCM-CSS). The overall evaluation is done in MATLAB. The performance of the hybrid IFCM-CSS is compared to other approaches based on some measures. The proposed Hybrid IFCM-CSS achieves the highest precision of 0.971, using the SBM-RGBD dataset.

Video anomaly detection has always been a challenging task in computer vision due to data imbalance and susceptibility to scene variations such as lighting and occlusions. In response to this challenge, this paper proposes an unsupervised video anomaly detection method based on an attention-enhanced memory network. The method utilizes a dual-stream network structure of autoencoders, enhancing the model’s learning ability for important features in appearance and motion by introducing coordinate attention mechanisms and variance attention mechanisms, emphasizing significant characteristics of static objects and rapidly moving regions. By adding memory modules to both the appearance and motion branches, the network structure’s memory information is reinforced, enabling it to capture long-term spatiotemporal dependencies in videos and thereby improving the accuracy of anomaly detection. Furthermore, by optimizing the network structure’s activation functions to handle negative inputs, it enhances its nonlinear modeling capabilities, enabling better adaptation to complex environments, including variations in lighting and occlusions, further improving the effectiveness of anomaly detection. The paper conducts comparative experiments and ablation studies using three public available datasets and various models. The results demonstrate that compared to baseline models, the AUC performance is improved by 3.9%, 4.7%, and 1.7% on UCSD Ped2, CHUK Avenue, and ShanghaiTech datasets, respectively. When compared with the other models, the average AUC performance is improved by 4.3%, 5.4%, and 6.2%, with an average improvement of 8.75% in the ERR metric, validating the effectiveness and adaptability of the proposed method. The code can be obtained at the following URL: https://github.com/AcademicWhite/AEMNet.

To overcome the limitations of unmanned vehicle global path planning with the sole objective of distance, a multi-objective particle swarm optimization strategy is proposed for indoor unmanned vehicle path planning. This strategy integrates objectives related to travel distance and cumulative turning angle. The traditional distance function is enhanced to accelerate algorithm convergence, and cumulative turning angle is introduced to construct a comprehensive function, meeting the demands of multi-objective navigation. The Pareto solution set concept is incorporated, and through the multi-objective particle swarm optimization algorithm, optimal paths for different travel objectives are identified, enhancing solution comprehensiveness. Experimental results validate the feasibility and effectiveness of the improved algorithm.

Research on continuous hand gesture recognition is motivated by applications of deaf people to provide effective communication between one another. Numerous techniques are adapted for effectively identifying the hand gesture. Moreover, the collection of data becomes a challenging task in the existing models. In some instances, hand gesture in low-resolution images becomes a complicated task. In this research work, a deep learning-based hand gesture detection model is designed using hand data points to uncover patterns that are used to convey information. Initially, hand gesture images are obtained from public sources. The collected inputs are preprocessed through Contrast-Limited Adaptive Histogram Equalization and filtering to eliminate the unnecessary blurs in the images. The preprocessed images undergo hand segmentation using four methods color space transformation, skin color detection, active contour, and morphological operation. The segmented image is subjected to data point feature extraction, where the Adaptive Weighted Scale-Invariant Feature Transform is utilized for further enhancement. The hand data point-extracted features are given to the recognition of hand gestures with the support of an attention-based hybrid network, where the Attention-based Hybrid 1D Convolutional Neural Network with Recurrent Neural Network recognizes the hand gestures from the hand data points. In the hybridization network, the values are optimized with the help of the investigated Adaptive Dandelion White Shark Optimizer to enhance recognition effectiveness. The test results are validated with the existing hand gesture recognition models using diverse evaluation metrics. The findings of the recommended method show 97% and 99% in terms of accuracy and NPV. Thus, the developed model is validated to outperform significant performance rather than the existing models.

This paper is dedicated to developing a cost-effective ground-moving object detection method in aerial videos. Without limitations on types, quantity, and distribution of moving objects, which were required by the previous methods, the proposed approach can detect various ground-moving objects in aerial videos captured through diverse flying states in various ground appearances. The proposed method is mainly composed of generation of the appropriate feature points, detection of moving objects, and target tracking. In the originality of our work, a novel detection strategy designed for ground-moving objects is based on change detection of Delaunay triangulation (CDDT) and a three-step motion vector search-based tracking algorithm is further exploited for enhancing the detection rate. Experimental results show that our method can achieve the detection rate of at least 95% (roughly similar to the famous existing state-of-the-art methods) and 0.03 second/frame (far less than the famous existing methods) using test videos (containing only several moving objects distributed in a sparse space) in the previous methods compared. Besides, the average detection rate of 86.81%, average false detection rate of 9.44%, and a frame rate of about 33$$fps can be obtained using our test videos captured in the complicated ground appearances. This result makes the proposed method more attractive for detecting various ground-moving objects in aerial videos, when compared to other approaches, and can also achieve cost-effective performance.

As a natural, intuitive and easy-to-learn mode of interaction, gesture plays an important role in communication. Hand detection, containing multimodal information, includes static and dynamic detection and involves intricate spatial relationship problems such as different hand sizes, complex joints, occlusion and self-occlusion. This study focused on a multimodal hand gesture recognition system based on YOLOv5 and MediaPipe with fused spatio-temporal features. First, the Mediapipe and OpenCV libraries were employed to implement hand keypoint detection. Subsequently, the human–computer interaction (HCI) of volume control was realized by identifying the distance between thumb and index. Finally, model training was conducted based on the YOLOv5 algorithm, and the recognition of different gesture categories was realized. The performance was evaluated and compared through YOLOv5s, YOLOv5m, and YOLOv5l. The gesture recognition system interface visualization was achieved through pyqt5. Experiments show that the average detection accuracy of the model is 99.4% and the recognition speed is around 0.2$$s.

Petroleum is a critical industrial material. In the process of oilfield development, the Fisher’s optimal segmentation method is often used to solve the problem of division and combination of layer series of development. However, when encountering the problem of long samples, this method has particularly obvious drawbacks due to the high storage requirements of the calculation process. Therefore, in practical work, the Fisher’s optimal binary segmentation method is generally used instead. Although it avoids storage problems, it is prone to falling into local optima. On the basis of analyzing the shortcomings of Fisher’s optimal segmentation and optimal binary segmentation algorithms, this paper processes a genetic clustering algorithm. This algorithm overcomes the problem of Fisher’s optimal binary segmentation algorithm easily falling into local optima and solves the problem of high storage capacity requirements in the calculation process of Fisher’s optimal segmentation algorithm. Taking the data of 17 subzones in S-2 8-11 sand groups of the Shahejie Formation of the Lower Tertiary in the Dongxin area as an example, this algorithm is applied to divide and combine layer series of development. The experimental results show that the algorithm’s partitioning results are reasonable and can optimize the selection of development layers and provide decision support for the production of oil and gas resources.

Classification of different brain tumors is challenging due to unpredictable variations in intra-inter-classes. Unlike existing methods which are not effective for images of complex backgrounds, the proposed work aims at accurate classification of diverse types of brain tumors such that an appropriate model can be used for disease identification. This study considers glioma, meningioma, no tumor, and pituitary tumors for classification. To achieve an accurate classification, we explore the Xception architecture layer, which involves flattening, dropout, and dense layer operations. The model extracts features based on shapes, spatial relationships, and structure of the image, discriminating between the different brain tumor images. The model is evaluated on a dataset of 7023 MRI images for classification. The results of a large dataset and comparative study with the existing methods show that the proposed method is better than state of the art in terms of classification rate. Specifically, our method achieves more than a 90% average classification rate, which is better than state of the art. The results on noisy and blurred datasets show that the proposed model is robust to noise and blur.

In the last years, endowing the machine with emotional and behavioral intelligence has been one of the most challenging issues in the human–computer interaction area. In this context, this paper investigates the detection of simultaneous fear emotion and deception behavior in speech. To do so, a set of 72 pitch-based features has been investigated first to recognize fear and deception separately. Then, different feature selection techniques have been used in order to select the most relevant ones that best discriminate between fear/nonfear and deception/nondeception classes. Next, a decision-level fusion approach based on the belief theory has been proposed to infer whether fear and deception are detected simultaneously. Simulation results carried on databases dealing with fear/nonfear emotions and deception/truth behaviors have shown classification results reaching 83.33% and 72.45% as accuracy rates for fear and deception classifiers, respectively. The proposed fusion approach has revealed a correspondence between fear emotion and deception behavior in speech modality.

Over the past decade, a wide range of brain–computer interface (BCI) applications with artificial intelligence (AI) assistance have emerged. While using a head-mounted electroencephalogram (EEG) device for BCI application, it often requires a user to wear the device close to the scalp so that clear EEG signals can be obtained from the head directly. Those signals can be used to determine a user’s mental state. Due to the head size differences between Chinese and European users, as well as the differences between Chinese users themselves, EEG headsets with fixed-electrode-point will enable some electrodes fail to capture a user’s EEG data accurately. Combined with previous research and related design methodology, the design of a head-mounted EEG device is proposed in this paper. In this study, Chinese youth are regarded as the target user group. After evaluating different designs of device structures, the possible positions and movement methods of electrodes, an EEG headset with movable electrodes for young Chinese users is finally delivered.