Inter-frame Difference Method Research Articles

Moving Object Detection Method Based on the Fusion of Online Moving Window Robust Principal Component Analysis and Frame Difference Method

The accuracy of moving object detection has a great impact on the accuracy of extracting the shape center coordinates of moving workpieces. The classic inter-frame difference method has "cavity" and "double shadow" issues for workpieces with comparable internal colors. In order to solve this problem, a moving object detection algorithm combining the three-frame difference method and Online Moving Window Robust Principal Component Analysis (OMWRPCA) is proposed. By using the OMWRPCA to extract the background image in the current frame and comparing it to the previous and current frames, the "cavity" and "double shadow" problems are avoided as well as the effects of background pixels. This paper presents a case study of a visual sorting experiment bench in an "intelligent manufacturing production demonstration line". The experiments show that the workpiece shape center coordinates obtained by the improved moving object detection algorithm are closer to the actual value than those obtained by the traditional algorithm, and the F-measure scores are above 0.8, which are more accurate than the other two algorithms. It is compared with the traditional algorithm of the frame difference method and the Online Mixture of Gaussian Matrix Factorization (OMoGMF).

Characterization of Immediate Pressing Tactics in Soccer in the Age of Artificial Intelligence

Abstract This paper focuses on the use of feature extraction techniques as well as parameter estimation to analyze the immediate pressing tactics in soccer games. The motion target detection method is used to capture the movements of the soccer player. By setting the rotation angle of the point cloud, the soccer movement action is represented in the form of a coordinate system. By combining the inter-frame difference method and setting the motion image threshold, the motion target can be obtained. Utilize Hu moments to extract the features of soccer motion. Combine the center of mass and velocity of soccer motion to reduce the error rate of motion feature extraction. Pairwise quaternions are utilized to represent soccer motion parameters to improve motion estimation. The results show that the soccer team has the greatest success rate of practicing immediate pressing tactics in 3s-4s, and the success rate of applying immediate pressing tactics after 4s is significantly lower. Team C has the highest success rate of huddling with defensive immediate pressing tactics, which reaches 56.1%. The success rate of huddling is closest to that of team A and team B, which are 43.54% and 43.97%, respectively.

Detection of attack behaviour of pig based on deep learning

Attack behaviour detection of the pig is a valid method to protect the health of pig. Due to the farm conditions and the illumination changes of the piggery, the images of the pig in the videos are often being overlapped, which lead to difficulties in recognizing pig attack behaviour. We propose an improved YOLOX target detection model to overcome these difficulties. The improvements of the proposed model are: (1) the normalization attention mechanism is adopted to gain global information in the last block of the neck network and (2) the loss function IoU in YOLOX is replaced by DIoU to improve the detection accuracy. The pig attack behaviour considered in this paper includes the ear biting, the tail biting, the head to head collision and the head to body collision. The dataset is builded from the artificially observed attack video segments by using the inter-frame difference method. In the pig attack behaviour detection experiments, the improved YOLOX model achieves 93.21% precision which is 5.30% higher than the YOLOX model. The experiment results show that the improved YOLOX can realize pig attack behaviour detection with high precision.

Flight Parameter—Wind Vortex Characteristic Control Model of a Four-Multirotor Unmanned Aerial Vehicle Operating in Pesticide Spraying of Rice

The downwash airflow generated by the rotors can enhance the penetration of pesticide droplets, allowing them to penetrate deeper into the canopy and more comprehensively control pests. Downwash air will produce a “wind vortex” in plant canopy, and the parameters of a “wind vortex” represent the effect of pesticide deposition to a certain extent. To obtain the corresponding relationship between wind vortices and flight parameters and control the effect of pesticide spraying, this paper carried out unmanned aerial vehicle (UAV) flight experiments in the field. Wind vortices were generated in rice canopy by downwash airflow, and the parameters of wind vortices were obtained by identifying wind-vortex images using the inter-frame difference method. The wind-vortex parameter control model was established, which can calculate the altitude and speed of the UAV when applying pesticide according to the target wind-vortex parameter. The deviations in the altitude were determined to be 0.67 and 0.43 m, and the deviations in the speed were 0.29 and 0.35 m/s during downwind and headwind UAV operations, respectively. The model relation functions were established, and their accuracies were found to be 97.1%, 92.3%, 69%, and 58% (downwind), and 97%, 78.4%, 62%, and 57% (upwind), respectively, indicating that downwind UAV operation leads to a clear relation between the wind-vortex parameters and the UAV-flight parameters. The model establishes the corresponding relationship between the wind-vortex parameters and flight parameters, which provides a theoretical basis for studying the precise application control method of UAV.

(Retracted) Dynamic scene images-assisted intelligent control method for industrialized feeding through deep vision learning

In a recirculating aquaculture system (RAS), feeding is an important factor affecting the growth of breeding objects. The traditional feeding methods relied on manual experience, which resulted in high labor costs and bait waste. To deal with these challenges, this paper proposes a dynamic scene images-assisted intelligent control method for industrial feeding through deep vision learning. First, a feeding video is processed according to the interframe difference method to obtain the image of the feeding state of the fish. Then, a modified VGG16 model is developed to determine the feeding state of the fish, transform it into a binary classification problem, and calculate the feeding frequency of the fish. After that, residual bait detection is deployed by adapting the YOLOv5 model. The results of the feeding frequency and the residual bait detection are then used to develop an intelligent feeding strategy to improve the growth rate of the fish and the conversion rate of the bait. Experimental tests on real-world scene images showed that the accuracy of identifying the feeding state by the modified VGG16 model reaches 92.4%. Through the verification of the medium-size RAS, compared with the traditional feeding method, the intelligent feeding method significantly saves manpower and reduce 15% of bait waste.

Ice detection and tracking by the infrared thermography based on the electro-impulse de-icing system

An infrared thermography method was proposed, which exploited the electro-impulse de-icing system as electromagnetic excitation to detect ice shape and track ice motion. The coupling physical fields of circuit, electromagnetic, stress and heat were expounded theoretically and numerical simulated to verify the feasibility of the measurement. With the use of image processing for ice samples of different system parameters and shapes, the original ice edge was obtained at discharge voltages of 100–200 V. As the residual icing state discussed at discharge voltages of 350–450 V, the trajectory of fragmented ice was tracked by interframe difference and centroid method, and de-icing effect was evaluated by the ratio of the Euclidean distance of the fragmented ice centroids as well. The proposed method presents a novel research insight into integrating ice detection and anti/de-icing technology of aircrafts. It can also be referenced for infrared thermography detection and fragmented ice tracking via heat sources.

Detection of Rockfalls on Tunnel Faces Using Extracted Moving Objects, Excavation Point Estimation, and Generation of Trajectory Images

Background: Tunnels are constructed at various locations for infrastructural purposes. During tunnel construction, industrial accidents have occurred due to rockfalls at tunnel faces. In addition, it has been confirmed that large rockfalls are precursors to tunnel collapses. Visual monitoring is currently used to detect such rockfalls. However, there are limitations to visual monitoring, and monitoring equipment is needed. Objective: In existing research, the inter-frame difference method of image processing and laser measurement methods have been proposed. However, there are difficulties in monitoring the entirety of the tunnel face using these methods. Thus, we propose methods that can overcome these difficulties and accurately detect rockfalls and identify where they occur. Methods: In this study, we propose a method for detecting rockfalls by combining the extraction of moving objects on a tunnel face and the estimation of excavation points. To identify the location of the rockfalls, rockfall trajectory images were generated. Results: Through experiments conducted during excavation, it was confirmed that the proposed method could correctly identify and detect only rockfalls in real time and identify the locations where they occurred. Conclusion: In this study, only rockfalls of 52 mm x 71 mm in size are detected in real time. It can enable workers to evacuate and prevent industrial accidents. In addition, by identifying the location of rockfalls, it is possible to know the danger level of the tunnel face.

Detecting falling rocks by estimating excavation points using single color markers

Tunnels have been constructed in various places for transportation and lifelines. During tunnel construction, the industrial accidents have occurred due to falling rocks from the tunnel face. A large amount of falling rocks is confirmed in the precursor of tunnel collapse. Therefore, it is necessary to detect falling rocks to prevent industrial accidents and to grasp of the situation on tunnel face. As conventional methods, the inter-frame difference method and the laser measurement method were proposed. However, those methods were difficult to monitor the entire tunnel face and detected moving objects other than falling rocks. In this paper, we propose a falling rocks detection method that combines the moving object detection method on the tunnel face and the estimation method of excavation points using single color markers. It was confirmed that only falling rocks were detected during excavation experiment.

Design of Sports Image Contour Feature Acquisition System Based on the Background Subtraction Method

Moving target detection (MTD) is one of the emphases and difficulties in the field of computer vision and image processing. It is the basis of moving target tracking and behavior recognition. We propose two methods are improved and fused, respectively, and the fusion algorithm is applied to the complex scene for MTD, so as to improve the accuracy of MTD in complex and hybrid scenes. Using the main idea of the three-frame difference image method, the background difference method and the interframe difference method are combined to make their advantages complementary to overcome each other’s weaknesses. The experimental results show that the method can be well adapted to the situation of periodic motion interference in the background, and it can adapt to the situation of sudden background changes.

An Adaptive Space Target Detection Algorithm

In space target monitoring system, due to the interference of noise, stray light and the limitations of the image acquisition system, there are nonuniform background and smear phenomena in star images. Moreover, there are also a large number of background stars in star images. All these factors lead to the difficulty of space target detection. In order to improve the detection performance, an adaptive space target detection algorithm is proposed in this letter. Firstly, in order to suppress the complex background interference in the star image, a window adaptive bidirectional one-dimensional (1-D) median filtering method is proposed, whose window size is set based on the maximum star size in the first star image. Then, background correction is performed by bidirectional 1-D median filter. Secondly, in order to improve the reliability of space target segmentation, an improved inter-frame difference method is proposed, which consists of two parts: 1) the bright line residue is eliminated based on a horizontal 1-D median filter; 2) the target segmentation threshold is adaptively set based on the Kalman filter method. Compared with existing algorithms, our proposed method can detect space targets quickly with low false alarm rate and high detection rate under complex background conditions.

Study on the Automatic Basketball Shooting System Based on the Background Subtraction Method

There are many drawbacks such as clustering, background updating, inaccurate testing results, and low anti-interference performance in traditional moving target detection theory. In our study, a background subtraction method to automatically capture the basketball shooting trajectory was used to eliminate the drawbacks of the fixed-point shooting system such as cumbersome installation and time and manpower consumption. It also can improve the accuracy and efficiency of moving target detection. We also synthetically compared to common methods including the optical flow method and interframe difference method. Results showed that the background subtraction method has better accuracy with an accuracy rate over about 90% than the interframe subtraction method (88%) and the optimal flow method (85%) and presents excellent robustness with considering variable speed and nonrigid objects. Meanwhile, the automatic detection system for basketball shooting based on background subtraction is built by coupling background subtraction with detection characteristics. The system detection speed built is further accelerated, and the image denoising is improved. The trajectory error rate is about 0.3, 0.4, and 0.5 for the background subtraction method, interframe subtraction method, and optimal flow method, respectively.

Detection of Body Behavior Characteristics in Sports Training Based on Grey Relational Model

In order to deal with the problems of low detection accuracy and efficiency existing in traditional detection methods of body behavior characteristics in sports training, this paper proposes a new detection method of body behavior characteristics in sports training based on the grey correlation model. According to this method, the foreground binary image is obtained by the interframe difference method, and the action information and contour information are extracted, firstly. And then, the behavior feature descriptor is obtained. After the description of body behavior characteristics in sports training is completed, the grey correlation coefficient is calculated, and the complete observation equation of time delay estimation of image grey value under complex sports training background is established to complete the detection of body behavior characteristics. The experimental results show that, compared with the traditional methods, the detection accuracy and efficiency of the text method can always maintain a high level, indicating that the practical application performance of this method is strong. In fact, the grey analysis does not attempt to find the best solution but does provide techniques for determining a good solution.

Experimental Discussion on Fire Image Recognition Based on Feature Extraction

Video image-based fire detection technology can overcome some shortcomings of traditional fire detection, and has a good development prospect. This paper summarizes the basic principles of image-based fire detection, and analyzes the main features of fire combustion images. According to these features, firstly, the interframe difference method and the watershed algorithm are used to extract the suspected fire image area which may occur. Then, the features of flame image in early fire stage, such as increasing flame area, fluttering edge, irregular shape and flame color, are used as fire recognition criteria. Meanwhile, various image processing technologies and algorithms are used to extract the four main features of the fire, so as to eliminate various sources of interference and further determine whether a fire has occurred. Finally, a variety of different fuels were selected under indoor conditions to simulate fire experiments under different conditions, and the video was recorded. Fire recognition experiments were carried out using experimental videos and some videos found on the Internet. The experimental results show that the extraction and further recognition of suspected fire areas are both effective. However, the experimental simulation environment is relatively simple, and many theoretical and practical problems need to be further studied and solved.

Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

In order to improve the video image processing technology, this paper presents a moving object detection and tracking algorithm based on computer vision technology. Firstly, the detection performance of the interframe difference method and the background difference model method is compared comprehensively from both theoretical and experimental aspects, and then the Robert edge detection operator is selected to carry out edge detection of the vehicle. The research results show that the algorithm proposed in this paper has the longest running time per frame when tracking a moving target, which is about 2.3 times that of the single frame running time of the CamShift algorithm. The algorithm has high running efficiency and can meet the requirements of real-time tracking of a foreground target. The algorithm has the highest tracking accuracy, the time consumption is reduced, and the error of the tracking frame deviating from the real position of the target is the least.

Vision-based real-time vehicle detection and vehicle speed measurement using morphology and binary logical operation

In recent trends, digital information to the industrial integration for the intelligent transportation system (ITS) field is gaining importance for the researcher, academia, and industrial persons. Visual information helps to manage traffic systems in the industrial forum to build smart cities in developed countries. This paper presents vision-based real-time vehicle detection and Vehicle Speed Measurement (VSM) using morphology operation and binary logical process for an unplanned traffic scenario using image processing techniques. Vehicle detection and VSM help to reduce the number of accidents and improve road network efficiency. The bounding box size for vehicle detection is flexible according to vehicles' sizes on the road. We test this system with different colors and dimensions for a selected Region of Interest (ROI). The ROI sets using the two-line approach. Here, we compare the proposed system with the inter-frame difference method and the blob analysis method with recall, precision, and F1 performance parameters.

Development of a novel motion capture and gait analysis system for rat locomotion

This paper proposes a marker-based motion capture system for rat motion detection and gait analysis. Motion capture in small animals such as rodents is more challenging than in humans because of their small bodies and rapid motion. Existing algorithms have poor applicability in rat motion capture in environments outside the studio. Moreover, gait analysis targeting on the rat is not performed by existing motion capture software. Our method consists of four procedures. First, Region of Interest (ROI) is extracted from the background using the inter-frame difference method and a depth filter. Second, a double-threshold marker detection method is used to detect markers in ROI and a marker shape filter is used to classify the markers. Third, a marker corrector is designed to modify missing and incorrect markers. Finally, a deep learning network is used to analyse the gait trajectory to classify rat as healthy, injured, or rehabilitated. The experimental result shows that marker recognition accuracy is 99.33%, higher than that of most existing software. The validation accuracy of the network is 100% and the loss is 0.0001. This method is conductive to the development of motion capture systems for small animals and research into the gait kinematics of rodents.

Machine Learning-Based Multitarget Tracking of Motion in Sports Video

In this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. In terms of moving target detection, the traditional detection algorithms are analysed theoretically as well as implemented algorithmically, based on which a fusion algorithm of four interframe difference method and background averaging method is proposed for the shortcomings of interframe difference method and background difference method. The fusion algorithm uses the learning rate to update the background in real time and combines morphological processing to correct the foreground, which can effectively cope with the slow change of the background. According to the requirements of real time, accuracy, and occupying less video memory space in intelligent video surveillance systems, this paper improves the streamlined version of the algorithm. The experimental results show that the improved multitarget tracking algorithm effectively improves the Kalman filter-based algorithm to meet the real-time and accuracy requirements in intelligent video surveillance scenarios.

Dynamic Recognition and Tracking of Barium Flow Field Based on Deglutition Video

Dynamic fluoroscopy was used to study swallowing in 84 adult patients. We proposed a method to extract the barium contrast region by improved interframe difference method, and to indirectly determine the position of epigmatous cartilage and cricopharyngeal muscle according to the location of barium meal. The method is easy to understand, and the extraction effect is good, with 85% probability of successful extraction. On the other hand, in order to evaluate the degree of deglutition difficulty, we used calculation to evaluate variables including displacement, duration, residual quantity, etc., except that there were gender differences in variables and external factors, such as illumination, most of the measurement variables had very good reliability. The experimental results showed that the moving target fluid barium was extracted by quantifying dynamic fluorescence deglutition and using gaussian based background subtraction algorithm. We conclude that this approach significantly reduces the time it takes clinicians to examine moving images. This paper describes how to study swallowing disorders by X-ray barium fluoroscopy, explains the application of interframe difference algorithm and background subtraction in deglutiography, and extracts the residual amounts in three locations: oral cavity, epiglottic cartilage and piriform fosse.

Defect Recognition in Radioscopic Image Sequences based on Bag-of-Words

Digital X-ray real-time imaging is a new technology in non-destructive detection using image sequences. At present, manual identification of defects is mainly used in practice, which is expertise-dependent and prone to errors. The existing automatic recognition method based on radiographic images mainly recognizes a single defect type for a single radiographic image, which is easy to produce a large number of false detections in non-defect areas, and cannot correctly identify images containing multiple types of defects. A defect recognition method based on Bag-of-Words is proposed, which uses the inter-frame difference method to obtain the moving target area, namely the defect area, and uses the SIFT operator based on the Bag-of-Words model to extract the defect features, and then the trained support vector machine (SVM) performs recognition. Experimental results show that the proposed method can effectively reduce false detections in non-defective areas. The defect recognition method correctly recognizes the defect category with a probability of 85.3%.

Application of human motion recognition technology in extreme learning machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.