Moving Object Detection Method Research Articles

Leveraging foreground–background cues for semantically-driven, training-free moving object detection

Deep learning algorithms and semantic cue-based methods are increasingly used to detect moving objects. The semantic label changes in pixel location over time are used for moving object detection in semantic cue-based studies. In this approach, traditional background subtraction is employed only when there is a lack of semantic background. However, the change in semantic information in each pixel is insufficient for accurate detection of moving objects due to similar labels assignments for different objects and limited use of pixel intensity data. Moreover, challenges arise in detecting moving objects, such as a lack of diversity in labeled objects, an uneven distribution of labels or classes, and an insufficient amount of unseen datasets. In this study, we propose the Moving Semantic Object Detector (MSemOD), a train-less moving object detection method based on a traditional method enhanced with semantic information provided by pre-trained semantic segmentation models. The proposed method includes a region-based decision step, utilizing the traditional methods and semantic information as spatial–temporal cues. Threshold values are applied in the method to determine the temporal and spatial movements of each segment in the image. To improve the consistency of the semantic information, the proposed method converts the semantic segmentation output into a semantic probability map by enhancing the temporal and spatial state. The performance of the proposed method is evaluated on the LASIESTA, CDNet2014, and the BMC datasets and the results are compared with the state-of-the-art algorithms. The results show that using region-based semantic information can significantly reduce false positives and enhance foreground–background segmentation in moving object detection.

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.

Infrared Moving Small Target Detection Based on Space–Time Combination in Complex Scenes

In the infrared small target images with complex backgrounds, there exist various interferences that share similar characteristics with the target (such as building edges). The accurate detection of small targets is crucial in applications involving infrared search and tracking. However, traditional detection methods based on small target feature detection in a single frame image may result in higher error rates due to insufficient features. Therefore, in this paper, we propose an infrared moving object detection method that integrates spatio-temporal information. To address the limitations of single-frame detection, we introduce a temporal sequence of images to suppress false alarms caused by single-frame detection through analyzing motion features within the sequence. Firstly, based on spatial feature detection, we propose a multi-scale layered contrast feature (MLCF) filtering for preliminary target extraction. Secondly, we utilize the spatio-temporal context (STC) as a feature to track the image sequence point by point, obtaining global motion features. Statistical characteristics are calculated to obtain motion vector data that correspond to abnormal motion, enabling the accurate localization of moving targets. Finally, by combining spatial and temporal features, we determine the precise positions of the targets. The effectiveness of our method is evaluated using a real infrared dataset. Through analysis of the experimental results, our approach demonstrates stronger background suppression capabilities and lower false alarm rates compared to other existing methods. Moreover, our detection rate is similar or even superior to these algorithms, providing further evidence of the efficacy of our algorithm.

Stereo SLAM in Dynamic Environments Using Semantic Segmentation

As we all know, many dynamic objects appear almost continuously in the real world that are immensely capable of impairing the performance of the majority of vision-based SLAM systems based on the static-world assumption. In order to improve the robustness and accuracy of visual SLAM in high-dynamic environments, a real-time and robust stereo SLAM system for dynamic scenes was proposed. To weaken the influence of dynamic content, the moving-object detection method was put forward in our visual odometry, and then the semantic segmentation network was combined in our stereo SLAM to extract pixel-level contours of dynamic objects. Then, the influences of dynamic objects were significantly weakened and the performance of our system increased markedly in dynamic, complex, and crowed city spaces. Following experiments with both the KITTI Odometry dataset and in a real-life scene, the results showed that our method could dramatically decrease the tracking error or drift, and improve the robustness and stability of our stereo SLAM in high dynamic outdoor scenarios.

Video Steganography Technique Based on Enhanced Moving Objects Detection Method

Video steganography has become a popular option for protecting secret data from hacking attempts and common attacks on the internet. However, when the whole video frame(s) are used to embed secret data, this may lead to visual distortion.
 Materials and Methods:
 This work is an attempt to hide sensitive secret image inside the moving objects in a video based on separating the object from the background of the frame, selecting and arranging them according to object's size for embedding secret image. The proposed approach reverses the secret image bits and uses XOR technique between the reversed bits and the detected moving object bits for embedding. The proposed approach provides more security and imperceptibility as the moving objects are used for embedding, so it is difficult to notice the changes in the moving objects instead of using background area for embedding in the video. Further development to the proposed approach in the area of video steganography has been done by applying spatial model in combination with statistical model. Additional LSB styles have been also applied to evaluate the ability of the proposed approach in detecting moving objects. In addition to evaluating the robustness of the proposed approach against different attacks such as salt and pepper noise and median filtering.
 Results:
 The experimental results showed the better visual quality of the stego video with PSNR values exceeding 70 dB, this indicates that the proposed method works without causing much distortion in the original video and transmitted secret message.
 Conclusion:
 The experimental proof of the proposed approach can successfully detect and embed secret image. Also, it provides more security and imperceptibility as the data was hidden in the moving objects and the updates in the moving objects are difficult to notice rather than the static region in a video

A Dual-Input Moving Object Detection Method in Remote Sensing Image Sequences via Temporal Semantics

Moving object detection in remote sensing image sequences has been widely used in military and civilian fields. However, the complex background of remote sensing images and the small sizes of moving objects bring great difficulties for effective detection. To solve this problem, we propose a real-time moving object detection method for remote sensing image sequences. This method works by fusing the semantic information from a single image extracted by the object detection branch with the motion information of multiple frames extracted by the motion detection branch. Specifically, in the motion detection branch, we design a motion feature enhancement module (MFE) to improve the interframe motion information. Then, we design a Motion Information Extraction network (MIE) to extract motion information. Finally, the moving object information is directly output by fusing the motion and semantic information extracted by the object detection branch. Based on the experimental results of the two datasets, the proposed method achieves an accuracy rate of 93.21%, a recall rate of 92.72%, an average frame rate of 25.25 frames (fps), and a performance of 96.71% in terms of AP@0.5. The performance of the proposed method is better than that of other methods, and the overall detection effect is better; therefore, it meets the needs of the detection task.

Moving object detection method with motion regions tracking in background subtraction

Moving object detection method with motion regions tracking in background subtraction

Moving Object Detection Based on Weighted Kernel Norm and Saliency Constrained RPCA

Focusing on the issue that the accuracy of object detection is reduced when the foreground target moves slowly and there is interference in the background, a moving object detection method based on weighted kernel norm and saliency constraint RPCA is proposed. In the new method, the weighted kernel norm is used to restore a relatively clean background, which is helpful to separate the slow moving target from the background. The l 1 norm is used to constrain the sparsity of moving objects, and the Frobenius norm is used to detect noise, and a saliency constraint is introduced to detect slow-moving objects. Experiments show that this method can effectively deal with the problems of slow foreground motion and background interference. Compared with the suboptimal algorithm, the average measured value F of the proposed algorithm is improved by 15%.

A survey of moving object detection methods: A practical perspective

Moving object detection is the foundation of research in many computer vision fields. In recent decades, a number of detection methods have been proposed. Relevant surveys mainly focused on the detection accuracy, while different practical detection tasks were not considered. However, in different application tasks, training modes and requirements are completely different. The purpose of this survey is to classify and evaluate recent moving object detection methods from a practical perspective. Two main types of practical application tasks are considered: the detection of seen scenes and the detection of unseen scenes. In the survey, two practical application tasks are defined, corresponding recent moving object detection technologies are reviewed, and future directions are suggested to provide references for researchers and technicians when choosing suitable algorithms in practical work.

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.

An Improved Algorithm for Moving Object Detection in YOLO UAV Videos

<p>Recently, moving object detection (MOD) in UAV (Unmanned Aerial. Vehicle) videos has been widely used in many fields. However, different objects and different algorithms often result in different detection accuracy. SSD (Single Shot MultiBox Detector) series and YOLO (You Only Look Once) version 5 are two popular object detection model, and their performance are always evaluated and compared with other improved method for optimizing detection accuracy. In this paper, an improved YOLO_v5 detection algorithm was proposed to further improve the detection accuracy. It adopted a cascaded inter-frame verification mechanism which is based on the neural network and uses spatial information and integrates object speed and direction as well to improve the detection accuracy of moving objects. To evaluate its performance, the open UAV video data from Stanford University was used to test the algorithms, and three types of moving objects were analyzed. The experimental results demonstrate that the proposed MOD method can improve the detection accuracy of small moving objects, which have a good application value, and can lay a foundation for subsequent related studies.</p> <p> </p>

DFC-D: A dynamic weight-based multiple features combination for real-time moving object detection

Real-time moving object detection is an emerging method in Industry 5.0, that is applied in video surveillance, video coding, human-computer interaction, IoT, robotics, smart home, smart environment, edge and fog computing, cloud computing, and so on. One of the main issues is accurate moving object detection in real-time in a video with challenging background scenes. Numerous existing approaches used multiple features simultaneously to address the problem but did not consider any adaptive/dynamic weight factor to combine these feature spaces. Being inspired by these observations, we propose a background subtraction-based real-time moving object detection method, called DFC-D. This proposal determines an adaptive/dynamic weight factor to provide a weighted fusion of non-smoothing color/gray intensity and non-smoothing gradient magnitude. Moreover, the color-gradient background difference and segmentation noise are employed to modify thresholds and background samples. Our proposed solution achieves the best trade-off between detection accuracy and algorithmic complexity on the benchmark datasets while comparing with the state-of-the-art approaches.

Moving Object Tracking Based on Sparse Optical Flow with Moving Window and Target Estimator.

Moving object detection and tracking are technologies applied to wide research fields including traffic monitoring and recognition of workers in surrounding heavy equipment environments. However, the conventional moving object detection methods have faced many problems such as much computing time, image noises, and disappearance of targets due to obstacles. In this paper, we introduce a new moving object detection and tracking algorithm based on the sparse optical flow for reducing computing time, removing noises and estimating the target efficiently. The developed algorithm maintains a variety of corner features with refreshed corner features, and the moving window detector is proposed to determine the feature points for tracking, based on the location history of the points. The performance of detecting moving objects is greatly improved through the moving window detector and the continuous target estimation. The memory-based estimator provides the capability to recall the location of corner features for a period of time, and it has an effect of tracking targets obscured by obstacles. The suggested approach was applied to real environments including various illumination (indoor and outdoor) conditions, a number of moving objects and obstacles, and the performance was evaluated on an embedded board (Raspberry pi4). The experimental results show that the proposed method maintains a high FPS (frame per seconds) and improves the accuracy performance, compared with the conventional optical flow methods and vision approaches such as Haar-like and Hog methods.

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.

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.

Moving-object detection and tracking by scanning LiDAR mounted on motorcycle based on dynamic background subtraction

This paper presents a method for moving-object detection and tracking (DATMO) in global navigation satellite systems (GNSS)-denied environments using a light detection and ranging (LiDAR) mounted on a motorcycle. Distortion in the scanning LiDAR data is corrected by estimating the pose (3D positions and attitude angles) of the motorcycle in a period shorter than the LiDAR scan period using normal distributions transform-based simultaneous localization and mapping (NDT-based SLAM) and the information from an inertial measurement unit (IMU) via the extended Kalman filter (EKF). The scan data of interest are extracted by subtracting the local environment map generated by NDT-based SLAM from the LiDAR scan data. Moving objects are detected from the scan data of interest using an occupancy grid method and are tracked with a Bayesian filter. Experimental results obtained from public road and university campus environments demonstrate the effectiveness of the proposed method.

A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks

Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost.

Detection and Adaptive Video Processing of Hyperopia Scene in Sports Video

In the research of motion video, the existing target detection methods are susceptible to changes in the motion video scene and cannot accurately detect the motion state of the target. Moving target detection technology is an important branch of computer vision technology. Its function is to implement real-time monitoring, real-time video capture, and detection of objects in the target area and store information that users are interested in as an important basis for exercise. This article focuses on how to efficiently perform motion detection on real-time video. By introducing the mathematical model of image processing, the traditional motion detection algorithm is improved and the improved motion detection algorithm is implemented in the system. This article combines the advantages of the widely used frame difference method, target detection algorithm, and background difference method and introduces the moving object detection method combining these two algorithms. When using Gaussian mixture model for modeling, improve the parts with differences, and keep the unmatched Gaussian distribution so that the modeling effect is similar to the actual background; the binary image is obtained through the difference between frames and the threshold, and the motion change domain is extracted through mathematical morphological filtering, and finally, the moving target is detected. The experiment proved the following: when there are more motion states, the recall rate is slightly better than that of the VIBE algorithm. It decreased about 0.05 or so, but the relative accuracy rate increased by about 0.12, and the increase ratio is significantly higher than the decrease ratio. Departments need to adopt effective target extraction methods. In order to improve the accuracy of moving target detection, this paper studies the method of background model establishment and target extraction and proposes its own improvement.

Independent Moving Object Detection Based on a Vehicle Mounted Binocular Camera

Accurate detection of independent moving objects is the key to ensure the safety of driverless vehicles during travelling. In this study, a moving object detection method based on a vehicle-mounted binocular camera is proposed. Firstly, a feature matching points selection strategy is designed for high accuracy camera ego-motion estimation. Then, the residual optical flow filed generated only by moving objects is estimated by taking into account the camera ego-motion and global mixed optical flow. A dynamic threshold segmentation method based on disparity is proposed to separate the moving regions from the residual optical flow field. Finally, 3D and 2D information are combined for extracting each single moving object from moving regions. The innovation of the proposed method is that it can detect any type and any size of moving object theoretically by using the dense motion information of the images. By taking the data in KITTI database as the test samples, the detection precision of the moving objects using the proposed algorithm can reach up to 91% without considering tracking strategy.

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.