Moving Object Detection Research Articles

A neural mechanism for detecting object motion during self-motion.

Detection of objects that move in a scene is a fundamental computation performed by the visual system. This computation is greatly complicated by observer motion, which causes most objects to move across the retinal image. How the visual system detects scene-relative object motion during self-motion is poorly understood. Human behavioral studies suggest that the visual system may identify local conflicts between motion parallax and binocular disparity cues to depth and may use these signals to detect moving objects. We describe a novel mechanism for performing this computation based on neurons in macaque middle temporal (MT) area with incongruent depth tuning for binocular disparity and motion parallax cues. Neurons with incongruent tuning respond selectively to scene-relative object motion, and their responses are predictive of perceptual decisions when animals are trained to detect a moving object during self-motion. This finding establishes a novel functional role for neurons with incongruent tuning for multiple depth cues.

Moving scene-based video segmentation using fast convolutional neural network integration of VGG-16 net deep learning architecture

Video and object segmentation are considered significant research topics in image-video processing. The techniques and mathematical models involved in detecting and segmenting objects employ several modules of different high-level approaches developed for video analysis, object extraction, classification, and recognition. Moving object detection is important in various applications like video surveillance, moving object tracking. This paper proposes video segmentation of moving scene using fast convolutional neural network with VGG-16 net architecture which improves the accuracy. This developed method based on CNN sparsely represents foreground, background, and segmentation mask, which is used in reconstructing the original images. The feed-forward network-trained videos are applied for object detection in a single image with co-segmentation approach where videos or image collections are required as the input. The segmentation is performed through comparative analysis of real-time DAVIS dataset. The results of the experiment show the efficiency of this proposed method which is tested and compared with the existing techniques such as convolution neural network, [Formula: see text]-nearest neighbors, and artificial neural network by the parameters, namely accuracy, precision, recall, and F1-Score. The proposed technique has been improved in terms of accuracy by 97.8%, precision by 94%, recall by 87.9%, and F1-Score by 83.8%.

XDMOM: A Real-Time Moving Object Detection System Based on a Dual-Spectrum Camera.

A low-cost and power-efficient video surveillance system, named XDMOM, is developed for real-time moving object detection outdoors or in the wild. The novel system comprises four parts: imaging subsystem, video processing unit, power supply, and alarm device. The imaging subsystem, which consists of a dual-spectrum camera and rotary platform, can realize 360-degree and all-day monitoring. The video processing unit uses a power-efficient NVIDIA GeForce GT1030 chip as the processor, which ensures the power consumption of the whole system maintains a low level of 60~70 W during work. A portable lithium battery is employed to supply power so that the novel system can be used anywhere. The work principle is also studied in detail. Once videos are recorded, the single-stage neural network YOLOv4-tiny is employed to detect objects in a single frame, and an adaptive weighted moving pipeline filter is developed to remove pseudo-targets in the time domain, thereby reducing false alarms. Experimental results show that the overall correct alarm rate of the novel system could reach 85.17% in the daytime and 81.79% at night when humans are monitored in real outdoor environments. The good performance of the novel system is demonstrated by comparison with state-of-the-art video surveillance systems.

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.

MOVING OBJECTS DETECTION, CLASSIFICATION AND TRACKING OF VIDEO STREAMING BY IMPROVED FEATURE EXTRACTION APPROACH USING K-SVM

The computer vision plays a vital role in variety of applications such as traffic surveillance, robotics, human interaction devices, etc. The video surveillance system has designed to detect, track and classify the moving objects. The moving object detection, classification and tracking of video streaming has various challenges, which utilizes various novel approaches. The existing work uses spatiotemporal feature analysis using sample consistency algorithm for moving object detection and classification. It is not performed well with the complex scene on the video. The binary masking representation of moving object is the challenging task for the researchers. These video streams are partitioned based on the frames, shots, and scenes; here the proposed research work utilizes kernel-Support Vector Machine learning technique for moving object detection and tracking. In this approach, the MIO-TCD DATASET is used for moving object detection. Here the feature extraction is the major part of foreground and background analysis in the video streaming, which utilizes the vehicle features based video data. The SURF (Speeded-Up Robust Feature) feature is used to recognize/register the object and it also used for classification of moving objects. Here the optical flow method is to quantify the relative motion of object in the video streams. Based on the differences on the partitioned frames, the optical flow features hold the object for measuring the pixel of the moving objects. The feature extraction process is improved by combining feature class with intensity level of optical flow result, which makes the gradient analysis of first order derivative function. The proposed method achieves the result of recall, precision, and f-measures than the existing work. The proposed method is done with the help of MATLAB 2018a. Keywords: Computer Vision and Pattern Recognition; Kernel-SVM; SURF features; Optical Flow; Texture feature; Moving object detection, tracking and classification;

Moving Object Tracking Using Multiple Cameras in Network

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

Self-Driving Car

Abstract: Every person in this world is concerned about being safe. Increasing safety and reducing road accidents, thereby saving lives are of great interest in the context of Advanced Driver Assistance Systems. Among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. In driving assistance systems, obstacle detection especially for moving object detection is a key component of collision avoidance. Many sensors can be used for obstacle detection and lane detection, such as laser, radar and vision sensors. The most frequently used principal approach to detect road boundaries and lanes using a vision system on the vehicle. Detecting all kinds of obstacles on the road, mainly include the IPM (Inverse Perspective Mapping) method. The system acquires the front view using a camera mounted on the vehicle then applying a few processes in order to detect the lanes and objects. A versatile methodology is used in order to detect the lanes and objects. In our research we have developed a simple heuristic method which is more robust in both lane detection object detection and tracking in video. In this method we use clustering methodology to group the detected points in case of lane detection. Heuristic gives effective results in detection and tracking of multiple vehicles at a time irrespective to the distance.

Image Shadow Detection and Removal Based on Region Matching of Intelligent Computing.

Shadow detection and removal play an important role in the field of computer vision and pattern recognition. Shadow will cause some loss and interference to the information of moving objects, resulting in the performance degradation of subsequent computer vision tasks such as moving object detection or image segmentation. In this paper, each image is regarded as a small sample, and then a method based on material matching of intelligent computing between image regions is proposed to detect and remove image shadows. In shadow detection, the proposed method can be directly used for detection without training and ensures the consistency of similar regions to a certain extent. In shadow removal, the proposed method can minimize the influence of shadow removal operation on other features in the shadow region. The experiments on the benchmark dataset demonstrate that the proposed approach achieves a promising performance, and its improvement is more than 6% in comparison with several advanced shadow detection methods.

Moving object detection via RPCA framework using non-convex low-rank approximation and total variational regularization

Moving object detection via RPCA framework using non-convex low-rank approximation and total variational regularization

Moving object detection for unseen videos via truncated weighted robust principal component analysis and salience convolution neural network

Moving object detection for unseen videos via truncated weighted robust principal component analysis and salience convolution neural network

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.

A multi features based background modeling approach for moving object detection

Background subtraction always remains an important and challenging task for different applications. Our previous work established the effectiveness of hybrid model by exploiting the oriented patterns present in a video sequences over other statistical method. To extend this approach further, we have proposed a novel approach herein by eliminating GLCM based features with an improved local Zernike moment and color components of intensity. These features are clubbed with the orientation based features extracted from angle co-occurrence matrices (ACMs) to model the background. Furthermore the Mahalanobis distance measure is replaced by Canberra distance to categorized foreground and background pixels, which significantly reduces the computational complexity of the proposed method due to the absence of covariance matrix measure. Comparative results have shown that our proposed method is effective than other competing method on different set of video sequences.

Effectiveness Of Moving Objects Detecting And Tracking In Airspace By Images In NearInfrared

Objects in nearinfrared (NIR) images can often have different linear scales and shapes than the same objects in optical images for visible spectrum (RedGreenBlue, RGB). Therefore they can require different computer vision methods for detection, tracking, and classification. This paper devoted to the methods by which the problems of moving objects detecting and tracking in NIR images are solved. The main characteristics of moving objects in image sequences are highlighted. Advantages and disadvantages of different methods for detecting and tracking objects in NIR images of airspace are considered and two of the most promising methods classes are selected. Studies have been carried out on the effectiveness of LucasKanade method, which is one of the methods of local optical flow, and the ORB method of scaleinvariant transformation of features when detecting and tracking moving objects in NIR images. In numerical experiments, more than 5000 NIR images containing moving objects of three types were used as well as three combinations of considered methods. It is shown which combination is the most accurate in the tasks of moving objects detection and tracking and can be used for airspace automatic operational control and management based on computer vision systems. Probably, other combinations of methods from the two considered classes also can help to increase accuracy of moving objects detection and tracking in airspace by NIR images.

A New Gradient-based Feature Extraction Method for Real-time Detection of Moving Objects Using Stereo Cameras

In this study, a gradient-based feature extraction method has been developed that can be used to detect moving objects in real-time applications such as unmanned ground or air vehicles. Feature extraction methods should produce fast results in real-time applications, as results need to be obtained between successive frames of video sequences within a limited time. For this reason, various sized image blocks were used in the developed method. The arithmetic mean (AM), geometric mean (GM), median (MD), and local contrast (LC) methods were used to calculate block intensities. In the stereo video stream, depth maps were also divided into blocks along with successive frames' R, G, and B channels. A novel feature extraction method was developed by calculating gradient-based relationships between adjacent blocks around the centre block. In experimental studies, the features extracted from stereo video frames using the proposed method were compared with Surf, Fast and Brisk methods according to their quantity, accuracy, and processing times, and more successful results were obtained. In addition, the moving object detection performance of the method was tested in real-time using an Unmanned Ground Vehicle.

Intelligent Target Detection and Tracking Algorithm for Martial Arts Applications

Moving object detection and tracking is the basis and key technology for intelligent video surveillance, human-computer interaction, mobile robot and vehicle visual navigation, industrial robot system, and other applications. It has important applications in intelligent monitoring, human-computer interaction, visual navigation, and so on. Intelligent technology can greatly facilitate the monitoring of countless targets. This paper adopts the random motion model to describe the motion state of the target. Based on wireless communication and information security, this paper studies the communication and information security of the intelligent system of martial arts target detection and tracking algorithm, and the basic idea of mean-shift tracking algorithm is to use gradient climbing of probability density to find local optimal. This paper uses the background subtraction method based on the vibe algorithm to obtain the binary image of the foreground object; the shadow detection algorithm is used to remove the shadow of the foreground image; the haar-like (Haar) feature is selected as the feature of motion detection, and the feature value of the rectangular area is quickly calculated to describe the adjacent image area. The difference between the features, the final result image, and then the intelligent system for analysis. Experimental data shows that the time consumed by the tracking algorithm is less than 20 ms, which can meet the real-time requirements of ordinary target tracking systems. The average processing time of the hybrid modeling method is 62.8 ms, and the detection rate is 15.92 frames/SEC. The results show that the algorithm improves the utilization of particles, greatly reduces the complexity, and reduces the degradation of the particle filter.

Feature Extraction Algorithm of Basketball Trajectory Based on the Background Difference Method

People are increasingly interested in moving object detection technology as a part of computer vision technology, which has grown in importance in recent years. Moving target detection technology is widely employed in military defense, security monitoring, medical examinations, intelligent transportation, and many other industries at the present time. A moving target detection system relies on rapid and accurate video picture segmentation to identify, locate, and analyze the target. The trajectory of a basketball player’s shooting motion and the extraction of features are important to increase the accuracy of basketball players’ shots. This study proposes an area growth algorithm-based approach to tracing the shooting motion of basketball players. The trajectory of the shot motion is captured using a video sensor image tracking approach. The corner points and the edge contours of the trajectory image are extracted using the edge contour feature extraction method, the feature extraction from the firing motion trajectory image is merged with the regional linear growth approach, and the corner points are then marked. Feature extraction improves basketball players’ shooting accuracy and trajectory control ability. Simulation results show that the prediction accuracy of basketball players’ shooting trajectories using this method can reach up to 100%, which improves the accuracy of motion trajectory extraction and enhances basketball players’ shooting motion control.

Learning dynamic background for weakly supervised moving object detection

Moving Object Detection (MOD) aims at extracting foreground moving objects in videos from static cameras. While low-rank based approaches have achieved impressive success in the MOD task, their performance remains limited on dynamics background scenes. The main reason is that dynamic clutters, e.g., swaying leaves and rippers, are easy to mix up with moving objects in the decomposition model which simply classify the sparse noise as foregrounds. In order to improve the generalization ability of low-rank based moving object detectors, we suggest adding an explicit dynamic clutter component in the decomposition framework with realistic dynamic background modeling. Then the dynamic clutter can be learned through object-free video data due to their self-similarity across time and space. Thus, the moving objects can be naturally separated by a tensor-based decomposition model which formulates the static background by a unidirectional low-rank tensor, learns the dynamic clutter by a two-stream neural network, and constrains moving objects with spatiotemporal continuity. To further provide a more accurate object detection result, an objectness prior is embedded into our model in an attention manner. Extensive experimental results on the challenging datasets of dynamic background clearly demonstrate the superior performance of our model over the state-of-the-art in terms of quantitative metrics and visual quality.

An Intelligent Motion Detection Using OpenCV

A computer vision system's basic goal is to detect moving things. For many applications, the performance of these systems is insufficient. One of the key reasons is that dealing with numerous restrictions such as environmental fluctuations makes the moving object detection process harder. Motion detection is a well-known computer technology associated with computer vision and image processing that focuses on detecting objects or instances of a specific class in digital photos and videos (for example, humans, flowers, and animals). Face detection, character recognition, and vehicle calculation are just a few of the well-studied applications of object motion detection. Object detection has a wide range of applications, including retrieval and surveillance. Object counting is a step after object detection that gets more exact and robust with the help of OpenCV. For object detection and counting, OpenCV includes a number of useful techniques. Object counting has a variety of applications in the fields of transportation, medicine, and environmental science, among others. Computer vision and image processing research is progressing rapidly and is being used to improve human lives. To avoid the drawbacks of current and newly established techniques, the suggested algorithm was tested on many open source images by imposing a single set of variables. The motion detection software system proposed in this paper allows us to see movement around an item or a visual area.

Moving Object Detection and Tracking by Event Frame from Neuromorphic Vision Sensors

Fast movement of objects and illumination changes may lead to a negative effect on camera images for object detection and tracking. Event cameras are neuromorphic vision sensors that capture the vitality of a scene, mitigating data redundancy and latency. This paper proposes a new solution to moving object detection and tracking using an event frame from bio-inspired event cameras. First, an object detection method is designed using a combined event frame and a standard frame in which the detection is performed according to probability and color, respectively. Then, a detection-based object tracking method is proposed using an event frame and an improved kernel correlation filter to reduce missed detection. Further, a distance measurement method is developed using event frame-based tracking and similar triangle theory to enhance the estimation of distance between the object and camera. Experiment results demonstrate the effectiveness of the proposed methods for moving object detection and tracking.