Moving Object Detection Algorithm Research Articles

Retraction Note: Moving object detection algorithm and motion capture based on 3D LiDAR

Retraction Note: Moving object detection algorithm and motion capture based on 3D LiDAR

Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

In order to understand human motion object detection methods, the author proposes a research on human motion object detection method based on Gaussian model. Firstly, traditional Gaussian models are unable to detect complex scenes or slow moving targets. Therefore, an improved Gaussian model based moving object detection algorithm is proposed. Secondly, multiple Gaussian models are used to represent the features of each pixel in the moving target image, and based on the matching of each pixel in the image with the Gaussian model, it is considered as a background point. Conversely, it is based on the principle of the foreground, and the Gaussian model is updated. Finally, by updating the foreground model and calculating short-term stability indicators, the detection effect of moving targets is improved. By determining the Gaussian distribution and pixel relationship, new parameters are set to construct the background model and eliminate the impact caused by sudden changes in lighting. The experimental analysis results show that this method can effectively detect and track moving targets, with good noise resistance, high clarity, and an accuracy rate of up to 99%. Compared with traditional Gaussian model methods, the improved method can more effectively detect moving targets and has better robustness.

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).

Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos

Object detection and classification have become prominent research topics in computer vision due to their applications in areas such as visual tracking. Despite advancements, vision-based methods for detecting smaller targets and densely packed objects with high accuracy in complex dynamic environments still encounter challenges. This paper introduces a novel and enhanced approach for hyperbolic shadow detection and object classification based on the Enhanced Moving Object Detection (EMOD) algorithm and an improved manta ray-based convolutional neural network optimized for search. In the preprocessing phase, the video data transforms into a sequence of frames, with polynomial adaptive antialiasing applied to maintain frame size and reduce noise. Additionally, an enhanced boundary area preservation algorithm improves the contrast of noise-free edited image sequences. To achieve high-precision detection of smaller objects, the Grib profile of each detected object is also tracked. Finally, a convolutional neural network method employing an enhanced Manta search optimization is deployed for target detection and classification. Comparative experiments conducted across diverse datasets and benchmark methods demonstrate significantly improved accuracy and expanded capabilities in detection and classification.

Moving object detection algorithm and motion capture based on 3D LiDAR

Moving object detection algorithm and motion capture based on 3D LiDAR

A Novel Moving Object Detection Algorithm Based on Robust Image Feature Threshold Segmentation with Improved Optical Flow Estimation

The detection of moving objects in images is a crucial research objective; however, several challenges, such as low accuracy, background fixing or moving, ‘ghost’ issues, and warping, exist in its execution. The majority of approaches operate with a fixed camera. This study proposes a robust feature threshold moving object identification and segmentation method with enhanced optical flow estimation to overcome these challenges. Unlike most optical flow Otsu segmentation for fixed cameras, a background feature threshold segmentation technique based on a combination of the Horn–Schunck (HS) and Lucas–Kanade (LK) optical flow methods is presented in this paper. This approach aims to obtain the segmentation of moving objects. First, the HS and LK optical flows with the image pyramid are integrated to establish the high-precision and anti-interference optical flow estimation equation. Next, the Delaunay triangulation is used to solve the motion occlusion problem. Finally, the proposed robust feature threshold segmentation method is applied to the optical flow field to attract the moving object, which is the. extracted from the Harris feature and the image background affine transformation model. The technique uses morphological image processing to create the final moving target foreground area. Experimental results verified that this method successfully detected and segmented objects with high accuracy when the camera was either fixed or moving.

Retracted: Optimization Algorithm of Moving Object Detection Using Multiscale Pyramid Convolutional Neural Networks.

[This retracts the article DOI: 10.1155/2023/3320547.].

Exploration of motion inhibition for the suppression of false positives in biologically inspired small target detection algorithms from a moving platform

Detecting small moving targets against a cluttered background in visual data is a challenging task. The main problems include spatio-temporal target contrast enhancement, background suppression and accurate target segmentation. When targets are at great distances from a non-stationary camera, the difficulty of these challenges increases. In such cases the moving camera can introduce large spatial changes between frames which may cause issues in temporal algorithms; furthermore targets can approach a single pixel, thereby affecting spatial methods. Previous literature has shown that biologically inspired methods, based on the vision systems of insects, are robust to such conditions. It has also been shown that the use of divisive optic-flow inhibition with these methods enhances the detectability of small targets. However, the location within the visual pathway the inhibition should be applied was ambiguous. In this paper, we investigated the tunings of some of the optic-flow filters and use of a nonlinear transform on the optic-flow signal to modify motion responses for the purpose of suppressing false positives and enhancing small target detection. Additionally, we looked at multiple locations within the biologically inspired vision (BIV) algorithm where inhibition could further enhance detection performance, and look at driving the nonlinear transform with a global motion estimate. To get a better understanding of how the BIV algorithm performs, we compared to other state-of-the-art target detection algorithms, and look at how their performance can be enhanced with the optic-flow inhibition. Our explicit use of the nonlinear inhibition allows for the incorporation of a wider dynamic range of inhibiting signals, along with spatio-temporal filter refinement, which further increases target-background discrimination in the presence of camera motion. Extensive experiments shows that our proposed approach achieves an improvement of 25% over linearly conditioned inhibition schemes and 2.33 times the detection performance of the BIV model without inhibition. Moreover, our approach achieves between 10 and 104 times better detection performance compared to any conventional state-of-the-art moving object detection algorithm applied to the same, highly cluttered and moving scenes. Applying the nonlinear inhibition to other algorithms showed that their performance can be increased by up to 22 times. These findings show that the application of optic-flow- based signal suppression should be applied to enhance target detection from moving platforms. Furthermore, they indicate where best to look for evidence of such signals within the insect brain.

Moving Object Detection Based on Fusion of Depth Information and RGB Features.

The detection of moving objects is one of the key problems in the field of computer vision. It is very important to detect moving objects accurately and rapidly for automatic driving. In this paper, we propose an improved moving object detection method to overcome the disadvantages of the RGB information-only-based method in detecting moving objects that are susceptible to shadow interference and illumination changes by adding depth information. Firstly, a convolutional neural network (CNN) based on the color edge-guided super-resolution reconstruction of depth maps is proposed to perform super-resolution reconstruction of low-resolution depth images obtained by depth cameras. Secondly, the RGB-D moving object detection algorithm is based on fusing the depth information of the same scene with RGB features for detection. Finally, in order to evaluate the effectiveness of the algorithm proposed in this paper, the Middlebury 2005 dataset and the SBM-RGBD dataset are successively used for testing. The experimental results show that our super-resolution reconstruction algorithm achieves the best results among the six commonly used algorithms, and our moving object detection algorithm improves the detection accuracy by up to 18.2%, 9.87% and 40.2% in three scenes, respectively, compared with the original algorithm, and it achieves the best results compared with the other three recent RGB-D-based methods. The algorithm proposed in this paper can better overcome the interference caused by shadow or illumination changes and detect moving objects more accurately.

CNN based Intelligent Framework to Predict and Detect Fire

Background/Objectives: Conventional detectors have a low degree of perfection and delicacy in relating the position of the fire and may give false admonitions. This exploration study is aimed at exercising a frame that's an agglomeration of traditional strategies and can be represented as a vision- grounded frame for intelligent finding and prediction of fire, using convolution neural systems. The performance of the said fire finding, and prediction framework is compared grounded on the perfection values under different evaluation scripts. Methods/Statistical analysis: First, image processing algorithms are used to build a fire detection framework. The continuous learning system uses the Deep CNN model and differentiates the fire data from the other data set. Better precision is gained with respect to the probability of the fire and smoke data. This helps to trigger an alarm at the right time. The Learning rate finder class will be used to derive the optimal learning rate. A continuous learning system tunes the accuracy of the classifier. When smoke is detected, movement of the smoke is taken into consideration. Using a moving object detection algorithm, suspected smoke regions are detected by the CNN algorithm. Data sets: More than twelve hundred plus augmented fire datasets were considered for this study, and two thousand plus non fire datasets were taken from the sources like ImageNet and Kaggle. Findings: Image processing technique helped to attain an accuracy of 85%. Addition of CNN improved the accuracy of the prediction to 95%. Novelty/Applications: An integrated framework enabling faster detection and prediction of fire and smoke using image processing techniques and using the processed image dataset, to classify with precision the smoke, fire, no-fire processed and augmented dataset.

Wide-area ship target recognition method based on motion and appearance features

Objective This paper proposes methods for better recognizing and positioning ships sailing in critical and wide-area waterways during monitoring operation. Methods Based on video surveillance technology, the joint use of the motion and appearance features of a ship target is carried out to realize a wide-area multi-dimensional recognition function via the combination of a background subtraction-based moving object detection algorithm and deep learning-based target recognition algorithm. In addition, the improved approaches including water ripple noise reduction, hierarchical moving object detection and window segmentation of channel monitoring image are put forward to further improve ship recognition accuracy. Results The fieldde monstration results show that the improved methods proposed in this paper allow the accurate recognition of a ship of any type or size on the monitoring screen, and the use of conventional cameras can also achieve the recognition and position of a ship navigating a water area within a radius of 3 km. Conclusions The improved methods proposed in this study have a range of advantages including wide-area monitoring, complete coverage of ship types and sizes, automatic target recognition and robust anti-interference ability.

Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.

Construction Crane Detection under Transmission Line Based on Improved Vibe Algorithm

In this paper, the moving object detection part of crane’s illegal construction detection under transmission line is studied, and two scenes are simulated. The Visual background extraction (Vibe) algorithm is selected as the moving object detection algorithm used in this paper. For the problems of the algorithm, including ghost phenomenon in detection results and the the presence of falsely detected foreground points under dynamic background, the Vibe algorithm is improved by the reconstructed background model, dynamic radius threshold and connected domain analysis. Results of the experiment show that ghost area in the detection results of the improved Vibe algorithm can quickly disappear, and the number of falsely detected foreground points is significantly reduced. Compared with the original Vibe algorithm, the average value of the comprehensive evaluation index F1 is increased by 5.8%, and the detection effect of moving object is significantly improved.

Moving Object Detection Algorithm based on Improved Visual Background Extractor

Aiming at the problems that the ViBe algorithm causes error detection when the camera jitter, ghosts appear when processing moving targets, and the detected moving targets are incomplete, an improved ViBe algorithm based on motion compensation is proposed. In the background modeling stage, the motion compensation method based on KLT is used to obtain background model to enhance the robustness of the algorithm to the dynamic background. In the foreground detection stage, combined the background model of the current pixel with 8 neighbor pixel background model, by the double discriminant algorithm to eliminate ghost areas caused by detecting the real background points as foreground points, introduce OTSU algorithm to obtain the optimal threshold for foreground detection. In the post processing stage, filter the connected components for segmentation masks and update models to limit the wrong background points scattered in the foreground.Finally, the connected domain is repaired by morphological opening operation. The results show that the algorithm can effectively eliminate the interference caused by camera jitter, and suppress the ghost phenomenon to obtain more accurate foreground images.

Bio-Inspired Video Enhancement for Small Moving Target Detection.

Moving targets at a very large distance from a camera appear small and of low contrast. The low signal-to-noise-ratio and the presence of clutter in the background degrade the detection performance of conventional moving object detection techniques. To address these challenges, we propose temporal pre-processing of video frames using a biologically-inspired vision model. The bio-inspired model consists of multiple layers of processing analogous to the photoreceptor cells in the visual system of small insects. The adaptive filtering mechanism in the photoreceptor cells suppresses clutter and expands the possible range of input signal changes which improves the target background contrast. We perform experiments on real world video sequences of small moving targets captured with a high bit depth, high resolution and high frame-rate camera. Experimental results show that the biological vision based pre-processing leads to improved detection performance when used in conjunction with a variety of computer vision based moving object detection algorithms. The temporal bio-processing alone has improved the area under the receiver operating characteristic (AUROC) curve of the best performing algorithm by 75.4%. Our results suggest that the bio-inspired pre-processing has strong potential to become a key component of a practical small target detection system.

Improved Algorithm of the Video Moving Object Detection Based on ViBE

Background: ViBE (Visual Background Extractor) is an algorithm with a variety of advantages in video moving object detection which utilizes a pixel-level background modeling. However, it is not suitable for distinguishing the scene of drastic change, adapts poorly to the sudden change of the illumination and may lost the object easily, because this algorithm uses a fixed threshold to distinguish the background from the foreground. Methods: In this paper, an improved ViBE algorithm is proposed, which an adaptive dynamic threshold method is introduced for classification of the foreground and the background in the changing scenes. When reconstructing the model it required for drastic change of illumination, Otsu algorithm is used to judge the threshold and select the appropriate frame to complete the reconstruction to achieve quick adapt to the light. Results: Experimental results show that the proposed algorithm has higher recall value, better precision and F value comparing to the original algorithm. The improved algorithm has the highest classification accuracy among other similar algorithms and therefore the improved algorithm significantly improves the detection results. Conclusion: After analyzing the principle of ViBE algorithm, this paper proposed improvements to it from two aspects to aim at its deficiency. Taking into account of the dynamic changes of different environments, the change factor was proposed to measure the dynamic degree of background. According to the value of the factor, adaptive clustering was obtained and clustering threshold was updated to improve the adaptability of the algorithm to the dynamic environments. The improved ViBE algorithm can find the appropriate frame to reconstruct model structure in the case of abrupt light change, which can quickly adapt itself to the light change and be more accurate in the object detection.

A Novel Method for Video Moving Object Detection Using Improved Independent Component Analysis

Moving object detection is a hot and difficult problem. How to separate the changed part from the unchanged one is the key. Also, the connection between the multi-dimensional information of input frames often plays an important role in moving object detection. In this article, we presented a novel and effective video moving object detection method. The innovation of this method lies in the calculation of the connection between multi-dimensional information considering time and space domain at the same time. At present, most moving object detection algorithms consider the time dimension and the space dimension in an independent manner. It is possible to cause some detection problems, such as, if the background information of a static or low-speed object is updated too quickly, only part of the foreground region is detected correctly. To avoid such deficiencies, the improved ICA method is adopted to separate the continuous multi-frame images in the time domain, and then to integrate these signals in the space domain. It could separate and detect the difference between the input signals to extract the correct foreground target, especially in some situations that insignificant changes happen. The proposed algorithm is named VTD-FastICA. A large number of experiments have shown that our method is more competitive than most of the moving object detection methods, especially in detecting long-span, occlusion environments, and objects with weak changes. Moreover, we also analyze the efficiency of our method in different situations, indicating that the proposed algorithm can be applied to real situations.

Recursive-learning-based moving object detection in video with dynamic environment

Moving object detection is a fundamental and critical task in video surveillance systems. It is very challenging for complex scenes having slow-moving and paused objects. This paper proposes a moving object detection algorithm which combines Gaussian mixture model with foreground matching. This algorithm is able to detect slow-moving and paused objects very effectively. This algorithm uses adaptive learning rate to deal with different rates of change in background. The performance of the proposed algorithm is evaluated on the challenging videos containing strong dynamic background and slow-moving and paused objects using standard performance metrics. Experimental results show that the proposed method achieves 25% average improvement in accuracy compared over existing algorithms.

An anti-recompression video watermarking algorithm in bitstream domain

Following the popularity of digital video application, video copying and dissemination have become very easy; however, this makes video hacking and piracy a potential threat in video communication. Video watermarking technology can solve the problem of copyright protection, and thus, it has been extensively researched. The robustness of the video watermarking algorithm in the bitstream domain is poor, especially the anti-recompression ability, since the watermarked video may be compressed again before transmitting. Considering this, this paper proposes a video watermarking algorithm in the bitstream domain based on moving object detection. To increase the robustness of the watermarking scheme, the moving macroblocks that belong to the moving object in each P frame can be identified via the moving object detection algorithm. Then, watermark embedding in the moving macroblocks is performed using codeword substitution to ensure the consistency of the bitstream. Moving object detection and watermark embedding are independent and are both carried out in the bitstream domain by partially decoding the bitstream; this avoids the complete decoding and reconstruction of the video, making the method to be highly efficient. The simulation results confirm that the proposed method is robust against recompression and has little impact on the video visual quality and no influence on the bit rate.

Unsupervised Moving Object Detection in Complex Scenes Using Adversarial Regularizations

Moving object detection (MOD) is a fundamental step in many high-level vision-based applications, such as human activity analysis, visual object tracking, autonomous vehicles, surveillance, and security. Most of the existing MOD algorithms observe performance degradation in the presence of complex scenes containing camouflage objects, shadows, dynamic backgrounds, and varying illumination conditions, and captured by static cameras. To appropriately handle these challenges, we propose a Generative Adversarial Network (GAN) based on a moving object detection algorithm, called MOD_GAN. In the proposed algorithm, scene-specific GANs are trained in an unsupervised MOD setting, thereby enabling the algorithm to learn generating background sequences using input from uniformly distributed random noise samples. In addition to adversarial loss, during training, norm-based loss in the image space and discriminator feature-space is also minimized between the generated images and the training data. The additional losses enable the generator to learn subtle background details, resulting in a more realistic complex scene generation. During testing, a novel back-propagation based algorithm is used to generate images with statistics similar to the test images. More appropriate random noise samples are searched by directly minimizing the loss function between the test and generated images both in the image and discriminator feature-spaces. The network is not updated in this step; only the input noise samples are iteratively modified to minimize the loss function. Moreover, motion information is used to ensure that this loss is only computed on small-motion pixels. A novel dataset containing outdoor time-lapsed images from dawn to dusk with a full illumination variation cycle is also proposed to better compare the MOD algorithms in outdoor scenes. Accordingly, extensive experiments on five benchmark datasets and comparison with 30 existing methods demonstrate the strength of the proposed algorithm.