Moving Object Detection Research Articles

Moving objects detection based on histogram of oriented gradient algorithm chip for hazy environment

<span>The most important aspects of computer vision are moving object detection (MOD) and tracking. Many signal-processing applications use regional image statistics. Compute-intensive video and image processing with low latency and high throughput is done with field programmable gate array (FPGA) image processing. Local image statistics are used for edge identification and filtering. The histogram of oriented gradients (HoG) algorithm extracts local shape characteristics by equalizing histograms. The objective of the work is to design the hardware chip of the algorithm and perform the simulation in the Xilinx ISE 14.7 simulation environment. The performance of the chip is evaluated in Modelsim 10.0 simulation software to check its feasibility. The performance of the chip design is estimated on Viretx-5 FPGA and compared with the MATLAB-2020 image processing tool-based response time. This form of tracking typically deals with identifying, anchoring, and tracking images and videos. A mask made from a cut-out of the object can then determine the plane's coordinates depending on its position. This type of object tracking is frequently utilized in the field of augmented reality (AR). The algorithm is most suited for object detection using hardware controllers in haze and foggy environments.</span>

Multiple moving object classification and tracking using DenCNN classifier

A Multiple moving object detection, tracking, and counting algorithm is mainly designed exclusively suitable for congested areas. The counting system can alleviate the betrayal performance in the crowded areas. Most of the existing methods developed for tracking and counting face serious challenges in detection due to high densities of the target. This condition urged the researchers to update the existing systems. The present methodology was designed to address such issues. In the present methodology, the contrast was initially enhanced between the objects and their backgrounds using a Double Plateau Histogram Equalization (DPHE). Then, the motion was estimated for the contrast-enhanced image to identify the moment of the object using the modified Adaptive Distance Covariance Rood Pattern Search (ADCRPS) algorithm. After that, the morphological operation was deployed to sharpen the images by removing all the unwanted things. Then, the features were extracted and important features were selected using the modified Chaotic Tent Shuffled Shepherd Optimization (CTSSO) Algorithm. With the selected features object, detection was done using the proposed Scaled Non-Monotonic Cauchy Dense Convolutional Neural Network (SNMC-DenCNN). The detected object was then tracked with the aid of Channel and Spatial Reliability Tracker (CSRT). Finally, the objects were counted by intersection over union (IOU) by explicitly computing the association between detected and tracked objects. Also, the experimental results showed the effectiveness and efficiency of the proposed system with enhanced accuracy.

Retraction Note: Application of adaptive moving object detection and edge calculation in police physical education training app course

Retraction Note: Application of adaptive moving object detection and edge calculation in police physical education training app course

Object Motion Detection Enabled by Reconfigurable Neuromorphic Vision Sensor under Ferroelectric Modulation.

Increasing the demand for object motion detection (OMD) requires shifts of reducing redundancy, heightened power efficiency, and precise programming capabilities to ensure consistency and accuracy. Drawing inspiration from object motion-sensitive ganglion cells, we propose an OMD vision sensor with a simple device structure of a WSe2 homojunction modulated by a ferroelectric copolymer. Under optical mode and intermediate ferroelectric modulation, the vision sensor can generate progressive and bidirectional photocurrents with discrete multistates under zero power consumption. This design enables reconfigurable devices to emulate long-term potentiation and depression for synaptic weights updating, which exhibit 82 states (more than 6 bits) with a uniform step of 6 pA. Such OMD devices also demonstrate nonvolatility, reversibility, symmetry, and ultrahigh linearity, achieving a fitted R2 of 0.999 and nonlinearity values of 0.01/-0.01. Thus, a vision sensor could implement motion detection by sensing only dynamic information based on the brightness difference between frames, while eliminating redundant data from static scenes. Additionally, the neural network utilizing a linear result can recognize the essential moving information with a high recognition accuracy of 96.8%. We also present the scalable potential via a uniform 3 × 3 neuromorphic vision sensor array. Our work offers a platform to achieve motion detection based on controllable and energy-efficient ferroelectric programmability.

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

Rapid automatic multiple moving objects detection method based on feature extraction from images with non-sidereal tracking

ABSTRACT Optically observing and monitoring moving objects, both natural and artificial, is important to human space security. Non-sidereal tracking can improve the system’s limiting magnitude for moving objects, which benefits the surveillance. However, images with non-sidereal tracking include complex background, as well as objects with different brightness and moving mode, posing a significant challenge for accurate multi-object detection in such images, especially in wide field-of-view telescope images. To achieve a higher detection precision in a higher speed, we proposed a novel object detection method, which combines the source feature extraction and the neural network. First, our method extracts object features from optical images such as centroid, shape, and flux. Then, it conducts a naive labelling based on those features to distinguish moving objects from stars. After balancing the labelled data, we employ it to train a neural network aimed at creating a classification model for point-like and streak-like objects. Ultimately, based on the neural network model’s classification outcomes, moving objects whose motion modes consistent with the tracked objects are detected via track association, while objects with different motion modes are detected using morphological statistics. The validation, based on the space objects images captured in target tracking mode with the 1-m telescope at Nanshan, Xinjiang Astronomical Observatory, demonstrates that our method achieves 94.72 per cent detection accuracy with merely 5.02 per cent false alarm rate, and a processing time of 0.66 s per frame. Consequently, our method can rapidly and accurately detect objects with different motion modes from wide-field images with non-sidereal tracking.

YOLODCC: Improved YOLOv8 combined with dynamic confidence compensation for lightweight moving object detection

AbstractMost multiple object tracking algorithms depend on the output of the detector. Aiming at the problem that the higher detection quality model is restricted by the computing power, and the robustness of the lightweight detection model is easily affected by motion blur, this paper proposes a lightweight moving object detector based on improved YOLOv8 combined with dynamic confidence compensation algorithm. The algorithm combines various technical means such as network structure optimization, lightweight design, self‐knowledge distillation, loss function improvement and dynamic confidence compensation. ByteTrack is used as a tracker to conduct experiments on PASCAL VOC07+12 data set and UA‐DETRAC test sequence. Compared with the baseline YOLOv8n+ByteTrack, the proposed algorithm improves the HOTA by 1.3% when the single frame tracking delay is reduced by 1.1%. Mostly tracked target is improved by 79.7%, mostly lost target is reduced by 10.9%, and the detection effect is better than the original detector and other popular object detectors. The YOLODCC model achieves a balance between lightweight and multi‐object motion blur.

Multi-spectral image fusion for moving object detection

This study delves into the problem of moving object detection in infrared and visible images. While existing approaches primarily focus on single-task detection using single-spectral image data, such as thermal infrared or visible images, they often ignore the differences between different spectral images and the interconnectedness of related tasks such as image fusion and segmentation. To tackle these problems, we present a novel multi-spectral image fusion network with quality and semantic awareness for moving object detection (MOD), particularly in scenarios where ground truth labels for both infrared and visible images are unavailable. Our approach fuses multi-spectral images, incorporating additional subtasks in the image fusion to obtain content and quality perception of infrared and visible images. In addition, we design a novel residual global perception module (RGPM) and multi-spectral fusion loss, which can capture more hidden features and contextual information across various scales. This enhanced capability leads to more precise detection and tracking of moving objects, particularly in challenging situations involving occlusions and dynamic backgrounds. Compared with single-spectral moving object detection optimization, the hurdles of utilizing deep learning for multi-spectral image fusion, e.g., without ground truth labels and harmful noise, are significantly mitigated. Extensive quantitative and qualitative comparative experiments demonstrate its effectiveness, robustness, and superior performance compared to contemporary methods. Concisely, the proposed fusion representation learning has gained 44.2%,31.2%,36.3%,41.6%,5.3%,31.2%,76.2% on EI, SF, DF, AG, MI, SD and Nabf metrics compared with the best competitors.

Moving object detection via feature extraction and classification

Abstract Foreground segmentation (FS) plays a fundamental and important role in computer vision, but it remains a challenging task in dynamic backgrounds. The supervised method has achieved good results, but the generalization ability needs to be improved. To address this challenge and improve the performance of FS in dynamic scenarios, a simple yet effective method has been proposed that leverages superpixel features and a one-dimensional convolution neural network (1D-CNN) named SPF-CNN. SPF-CNN involves several steps. First, the coined Iterated Robust CUR (IRCUR) is utilized to obtain candidate foregrounds for an image sequence. Simultaneously, the image sequence is segmented using simple linear iterative clustering. Next, the proposed feature extraction approach is applied to the candidate matrix region corresponding to the superpixel block. Finally, the 1D-CNN is trained using the obtained superpixel features. Experimental results demonstrate the effectiveness of SPF-CNN, which also exhibits strong generalization capabilities. The average F1-score reaches 0.83.

Moving object detection based on bioinspired background subtraction

Flying insects rely mainly upon visual motion to detect and track objects. There has been a lot of research on fly inspired algorithms for object detection, but few have been developed based on visual motion alone. One of the daunting difficulties is that the neural and circuit mechanisms underlying the foreground-background segmentation are still unclear. Our previous modeling study proposed that the lobula held parallel pathways with distinct directional selectivity, each of which could retinotopically discriminate figures moving in its own preferred direction based on relative motion cues. The previous model, however, did not address how the multiple parallel pathways gave the only detection output at their common downstream. Since the preferred directions of the pathways along either horizontal or vertical axis were opposite to each other, the background moving in the opposite direction to an object also activated the corresponding lobula pathway. Indiscriminate or ungated projection from all the pathways to their downstream would mix objects with the moving background, making the previous model fail with non-stationary background. Here, we extend the previous model by proposing that the background motion-dependent gating of individual lobula projections is the key to object detection. Large-field lobula plate tangential cells are hypothesized to perform the gating to realize bioinspired background subtraction. The model is shown to be capable of implementing a robust detection of moving objects in video sequences with either a moving camera that induces translational optic flow or a static camera. The model sheds light on the potential of the concise fly algorithm in real-world applications.

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.

Enhanced Object Detection in Autonomous Vehicles through LiDAR—Camera Sensor Fusion

To realize accurate environment perception, which is the technological key to enabling autonomous vehicles to interact with their external environments, it is primarily necessary to solve the issues of object detection and tracking in the vehicle-movement process. Multi-sensor fusion has become an essential process in efforts to overcome the shortcomings of individual sensor types and improve the efficiency and reliability of autonomous vehicles. This paper puts forward moving object detection and tracking methods based on LiDAR—camera fusion. Operating based on the calibration of the camera and LiDAR technology, this paper uses YOLO and PointPillars network models to perform object detection based on image and point cloud data. Then, a target box intersection-over-union (IoU) matching strategy, based on center-point distance probability and the improved Dempster–Shafer (D–S) theory, is used to perform class confidence fusion to obtain the final fusion detection result. In the process of moving object tracking, the DeepSORT algorithm is improved to address the issue of identity switching resulting from dynamic objects re-emerging after occlusion. An unscented Kalman filter is utilized to accurately predict the motion state of nonlinear objects, and object motion information is added to the IoU matching module to improve the matching accuracy in the data association process. Through self-collected data verification, the performances of fusion detection and tracking are judged to be significantly better than those of a single sensor. The evaluation indexes of the improved DeepSORT algorithm are 66% for MOTA and 79% for MOTP, which are, respectively, 10% and 5% higher than those of the original DeepSORT algorithm. The improved DeepSORT algorithm effectively solves the problem of tracking instability caused by the occlusion of moving objects.

Secure Deep Learning Framework for Moving Object Detection in Compressed Video

Secure Deep Learning Framework for Moving Object Detection in Compressed Video

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.

Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4

Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with the increasing number of machine learning strategies used for object detection, there has been a growing need for large datasets with accurate ground truth used for the training, usually demanding their manual labeling. Moreover, most of these deep strategies are supervised and only applicable for specific scenes with large computational resources needed. Alternatively, other object detection techniques such as classical background subtraction need low computational resources and can be used with general scenes. In this paper, we propose a new a reliable semi-automatic method that combines a modified version of the detection-based CNN You Only Look Once V4 (YOLOv4) technique and background subtraction technique to perform an unsupervised object detection for surveillance videos. In this proposed strategy, background subtraction-based low-rank decomposition is applied firstly to extract the moving objects. Then, a clustering method is adopted to refine the background subtraction (BS) result. Finally, the refined results are used to fine-tune the modified YOLO v4 before using it in the detection and classification of objects. The main contribution of this work is a new detection framework that overcomes manual labeling and creates an automatic labeler that can replace manual labeling using motion information to supply labeled training data (background and foreground) directly from the detection video. Extensive experiments using real-world object monitoring benchmarks indicate that the suggested framework obtains a considerable increase in mAP compared to state-of-the-art results on both the CDnet 2014 and UA-DETRAC datasets.

Self-powered noncontact triboelectric nanogenerators with microstructured square-loop surface and dielectric electron-blocking layer for far-distance motion perception and trajectory tracking

Continuous monitoring of indoor human activities is crucial for the healthcare of elder people or patients. The self-powered noncontact triboelectric nanogenerator (TENG) sensor has been recently developed as a promising noncontact sensing tool with many advantages, but its capability of detecting object motion in a far distance still remains inferior. Herein, by implementing multiple strategies of micro-structuring triboelectric surface, introducing dielectric layer, and using functional nano fillers, we develop a single-electrode-mode noncontact TENG sensor by constructing a triboelectric cobalt nanoporous carbon/Ecoflex nanocomposite layer with microstructured square-loop surface on top of a dielectric copper calcium titanate/Ecoflex nanocomposite layer with colossal permittivity. After in-depth optimization of the material selection, component composition and structure design, the developed sensor with effective electrode area of 6.25 cm2 can deliver a high output voltage of 29 V at 1 cm, and is capable of detecting motions at an extremely far distance of 6 m. Towards practical applications, the TENG unit for single-motion detection of mouth breathing, human walking, and basketball bouncing are demonstrated. Furthermore, multiple TENG units can be assembled into array to realize the complex trajectory tracking for the use of proximity touch pad, indoor human activity monitoring, and garage car parking. This work for the first time extends the TENG noncontact sensing capability to several meters, which is potential in the proximity motion detection and indoor activity monitoring.

Research on Object Motion, Fire and Gas Detection Using ESP32

Abstract: Working parents frequently worry about leaving their young children unattended at home, but thanks to technological advancements, it is now simpler to keep an eye on things and send information quickly. With the aid of the Internet of Things, parents may simply monitor their children's behavior within the home or keep an eye on guests at the entrance. By sensing motion and sending photographs on Telegram, this hardware-based application seeks to improve home security and kid safety by assisting in identifying any anomalous activity. This hardware-based architecture saves time and aids in monitoring in lieu of the owner. The fast expansion of industrial facilities and infrastructure is causing pollution, climate change, and other environmental problems. The suggested device uses humidity sensors, temperature detectors (DHT 11), gas detectors (MQ2, MQ7, and MQ135),and other sensors to identify the unfavorable gases present in the production process.

LENet: Lightweight and Effective Detector for Aerial Object

Aerial object detection is crucial in various computer vision tasks, including video monitoring, early warning systems, and visual tracking. While current methods can accurately detect normal-sized objects, they face challenges distinguishing small objects from cluttered backgrounds. Developing methods that can be deployed on edge devices to achieve fast, accurate, and energy-efficient performance is also an urgent challenge. This paper proposes a network for aerial object detection by incorporating an attention mechanism to enhance feature extraction and elevate the accuracy of aerial moving object detection. Additionally, we optimize the channel dimensions of the feature extraction framework, resulting in a reduction in model parameters, acceleration of inference speed, and alleviation of the computational burden. Ulteriorly, we optimize the Spatial Pyramid Pooling (SPP) module to enhance detection accuracy and processing speed. Inspired by the ResNet and RepVGG structures, we design a feature fusion module to combine early-extracted features, improving speed and accuracy. Based on the design mentioned above principles, we develop a neural network method with an impressively small model size of only [Formula: see text][Formula: see text]M. The proposed approach achieves state-of-the-art performance on five benchmark datasets. Besides its superior performance, our method demonstrates excellent throughput on edge computing devices. Experimental results show that even when running on low-performance computing devices, the CPU and GPU temperatures remain below 50∘C and achieve a detection speed of 14.8 frames per second (fps) and power consumption of only 2.9[Formula: see text]W. These findings suggest that a high-accuracy, low-power, low-latency, and low-memory footprint aerial object detection solution is achievable.

Moving Object Detection in Freely Moving Camera via Global Motion Compensation and Local Spatial Information Fusion.

Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds.

A novel artificial visual system for motion direction detection in color images

In recent years, convolutional neural networks (CNNs) have dominated the field of computer vision. Compared to traditional methods, these neural network algorithms exhibit strong biomimetic performance advantages in complex visual tasks due to their brain-like structure. However, because some necessary neural characteristics are ignored, these neural network algorithms differ greatly from the computational mechanisms of the brain. This paper starts with extracting basic visual features such as motion direction information from the brain and abstracts, generalizes and models a novel artificial visual system (AVS) for detecting object motion direction in color images based on existing relevant neurophysiological knowledge. We propose a mathematical model and quantification mechanism for each component neuron that generates motion direction selectivity in the visual system using dendritic neuron models, spiking neural network concepts and neurophysiological knowledge of retinal direction-selective ganglion cell pathways. The experiment is based on one million instances of object motion under different environments of noise-free, static and dynamic random noise, dynamic salt-and-pepper noise, dynamic Gaussion noise, and dynamic light changing. In comparison with 4 famous CNNs, LeNet-5, EfficientNetB0, ResNet18, and RegNetX-200MF, we test and verify AVS’s effectiveness, efficiency and strong generalization ability as well as other biomimetic performance advantages including high biological rationality, learning-free capability, interpretability and ease-of-use etc. AVS demonstrates that neuroscience still has important implications for guiding and promoting the development of artificial intelligence technology. Furthermore, AVS firstly provides a successful quantitative reference case study for further understanding motion direction selectivity and other primary cortical encoding characteristics in the brain.