Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

Intelligent Sensing and Control of Road Construction Robot Scenes Based on Road Construction

Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real-world monitoring,the process will use RTK-GNSS positional perception technology,by projecting the left side of the earth from Gauss-Krueger projection method,and then carry out the Cartesian conversion based on the characteristics of drawing;steering control system is the core of the electric drive unmanned module,on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles,the steering system key components such as direction,torque sensor,drive motor and other models are established,the joint simulation model of unmanned engineering vehicles is established,the steering controller is designed using the PID method,the simulation results show that the control method can meet the construction path demand for automatic steering.The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm,and never realize the construction-based path planning,and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm.With the collaboration of various modules,the automatic construction simulation results of this robot show that the design path and control method is effective.

Identifying Similar Operation Scenes for Busy Area Sector Dynamic Management

Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.

Eye movements during inspecting pictures of natural scenes for information to verify sentences

As eye tracking can be used to record moment-to-moment changes of eye movements as people inspect pictures of natural scenes and comprehend information, this paper attempts to use eye-movement technology to investigate how the order of presentation and the characteristics of information affect the semantic mismatch effect in the picture-sentence paradigm. A 3（syntax）×2（semantic relation） factorial design is adopted, with syntax and semantic relations as within-participant variables. The experiment finds that the semantic mismatch is most likely to increase cognitive loads as people have to spend more time, including first-pass time, regression path duration, and total fixation duration. Double negation does not significantly increase the processing difficulty of pictures and information. Experimental results show that people can extract the special syntactic strategy from long-term memory to process pictures and sentences with different semantic relations. It enables readers to comprehend double negation as affirmation. These results demonstrate that the constituent comparison model may not be a general model regarding other languages.

No Place for Adolescence to Rest——The Symbolic Scenes on Holden's Journey in The Catcher in the Rye

By releasing the book The Catcher in the Rye,J.D.Salinger received an immediate popularity of his writing career.Hissymbolic use of language has been thoroughly researched but the symbolic scenes which make up Holden's life stage,especiallythe symbolic connotations of ironic resting places in the novel,such as bed,couch and bedroom,has not been paid much attention.It tries to analyse the four scenes: on Holden's history teacher's bed,on the hotel bed with a prostitute,in his sister's bedroom,and on his English teacher's couch,and aims to discover his spiritual chaos as well as adolescent desires in the real world,demon-strating that there is no place for adolescent Holden to rest after he chooses his own stage of scenes in his life.

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Current researches mostly use traditional machine learning methods to extract features of weather scenes,and clustering algorithms to divide similar scenes.Inspired by the excellent performance of deep learning in image recognition,this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering(IDCEC),which uses the com-bination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image,retaining useful information to the greatest extent,and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area.Finally,term-inal area of Guangzhou Airport is selected as the research object,the method pro-posed in this article is used to classify historical weather data in similar scenes,and the performance is compared with other state-of-the-art methods.The experi-mental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather;at the same time,compared with the actualflight volume in the Guangz-hou terminal area,IDCEC's recognition results of similar weather scenes are con-sistent with the recognition of experts in thefield.

Three-Dimensional Scenes Restore Using Digital Image

Encryption and decryption method of three-dimensional objects uses holograms computer-generated and suggests encoding stage. Information obtained amplitude and phase of a three-dimensional object using mathematically stage transforms overlap stored on a digital computer. Different three-dimensional images restore and develop the system for the expansion of the three-dimensional scenes and camera movement parameters. This article talks about these kinds of digital image processing algorithms as the reconstruction of three-dimensional model of the scene. In the present state, many such algorithms need to be improved in this paper proposing one of the options to improve the accuracy of such reconstruction.

Automatic Mid-Level Concepts Clusteringfor Violent Movie Scenes Detection

This paper presents a novel system for violent scenes detection, which is based on machine learning to handle visual and audio features. MKL （Multiple Kernel Learning） is applied so that multimodality of videos can be maximized. The largest features of our system is that mid-level concepts clustering is proposed and implemented in order to learn mid-level concepts implicitly. By this algorithm, our system does not need manually tagged annotations. The whole system is trained on the dataset from MediaEval 2013 Affect Task and evaluated by its official metric. The obtained results outperformed its best score.

Analysis of the Writing Characteristics of the Title Music,Using“Scenes of Childhood”and“Children’s Garden”as Examples

In European thought and culture,there exists a group of passionate artists who are fascinated by the intention,passion,and richness of artistic expression.They strive to establish connections between different art forms.Musicians not only attempt to represent masterpieces through the language of music but also aim to convey subjective experiences of emotions and personal imagination to listeners by adding titles to their musical works.This study examines two pieces,“Scenes of Childhood”and“Children’s Garden”,and analyzes the different approaches employed by the composers in portraying similar content.

＂Montage＂ mentality on the art of painting study about figure painting scenes of convergence in the Five Dynasties and Song Dynasty

We often see different time and space scenes, different stories and different viewpoints change organically through one lens converge to form a complete film in the movie, and one of a series of the shearing lens elements and convergence is the montage technique. Such a method actually also often exists in different scenes in our ancient literature and different scenes and jumping thinking thoughts form a series of montage in the mind, and then it can be expressed through poetry. This case is the same as it in art, different time and spaces, the characters appear in a scene in a picture, and we distinguish them with a series of symbols to convergence in each scene. Therefore, we can say that if the montage was born after his birth of firms in the twentieth century, then it has existed since ancient time, and it has always existed in the fine arts, however, such thinking played an important role in the development of Chinese art, especially in the convergence and performance practices screen scene.

Ordinary Scenes Illustrate SpecialAtmosp here under Their Brushes——Appreciating the art ofZhou Tao,Chen Yi and Zhang Xin

In the past, many Chinese artists have beenafraid they would be criticized for lagging behindthe world in their conceptions. They are eager todo something unconventional or unorthodox. Theiractions are very similar with the trend Liu

Behind the Scenes

NPC deputies working on behalf of the people they represent are making a difference back home One of the true signs of spring is the arrival of swallows.But in Beijing,another sign heralds the return of springtime:thousands of deputies come to town to attend a series of two-week-long meetings,where a range of diverse issues are

Identification of Similar Air Traffic Scenes with Active Metric Learning

The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decision-making experience may be used to help controllers decide control strategies quickly.Considering that there are many traffic scenes and it is hard to label them all,in this paper,we propose an active SVM metric learning(ASVM2L)algorithm to measure and identify the similar traffic scenes.First of all,we obtain some traffic scene samples correctly labeled by experienced air traffic controllers.We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them.Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes.We verify the effectiveness of ASVM2L on standard data sets,and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China.The experimental results show that,compared with other existing methods,the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.

SA-FRCNN:An Improved Object Detection Method for Airport Apron Scenes

The airport apron scene contains rich contextual information about the spatial position relationship.Traditional object detectors only considered visual appearance and ignored the contextual information.In addition,the detection accuracy of some categories in the apron dataset was low.Therefore,an improved object detection method using spatial-aware features in apron scenes called SA-FRCNN is presented.The method uses graph convolutional networks to capture the relative spatial relationship between objects in the apron scene,incorporating this spatial context into feature learning.Moreover,an attention mechanism is introduced into the feature extraction process,with the goal to focus on the spatial position and key features,and distance-IoU loss is used to achieve a more accurate regression.The experimental results show that the mean average precision of the apron object detection based on SAFRCNN can reach 95.75%,and the detection effect of some hard-to-detect categories has been significantly improved.The proposed method effectively improves the detection accuracy on the apron dataset,which has a leading advantage over other methods.

Translation of the contrastive scenes and implication in River snow

This paper makes a brief analysis of the scenes and implication of the famous Ancient Chinese poem River snow. It makes a comparison and contrast of the five typical English versions from the perspective of the translation of its static and dynamic states. The paper also discusses the meaning of “du diao han jiang xue”, and how to better translate the key word “diao”.

A Study of Video Scenes Clustering Based on Shot Key Frames

In digital video analysis, browse, retrieval and query, shot is incapable of meeting needs. Scene is a cluster of a series of shots, which partially meets above demands. In this paper, an algorithm of video scenes clustering based on shot key frame sets is proposed. We use X^2 histogram match and twin histogram comparison for shot detection. A method is presented for key frame set extraction based on distance of non adjacent frames, further more, the minimum distance of key frame sets as distance of shots is computed, eventually scenes are clustered according to the distance of shots. Experiments of this algorithm show satisfactory performance in cor rectness and computing speed.

Analyzing Motion Patterns in Crowded Scenes via Automatic Tracklets Clustering

Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.