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Journal articles on the topic 'Motion object detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Yoo, Jisang, and Gyu-cheol Lee. "Moving Object Detection Using an Object Motion Reflection Model of Motion Vectors." Symmetry 11, no. 1 (2019): 34. http://dx.doi.org/10.3390/sym11010034.

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Moving object detection task can be solved by the background subtraction algorithm if the camera is fixed. However, because the background moves, detecting moving objects in a moving car is a difficult problem. There were attempts to detect moving objects using LiDAR or stereo cameras, but when the car moved, the detection rate decreased. We propose a moving object detection algorithm using an object motion reflection model of motion vectors. The proposed method first obtains the disparity map by searching the corresponding region between stereo images. Then, we estimate road by applying v-dis

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2

Huan, Ruo Hong, Xiao Mei Tang, Zhe Hu Wang, and Qing Zhang Chen. "Abnormal Motion Detection for Intelligent Video Surveillance." Applied Mechanics and Materials 58-60 (June 2011): 2290–95. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2290.

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A method of abnormal motion detection for intelligent video surveillance is presented, which includes object intrusion detection, object overlong stay detection and object overpopulation detection. Background subtraction algorithm is used to detect moving objects in video streams. Kalman filter is applied for object tracking. By the construction of relation matrix, the tracking process is divided into five statuses for prediction and estimation, which are object disappearing, object separating, new object appearing, object sheltering and object matching. The object parameters and predictive in

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Zhu, Juncai, Zhizhong Wang, Songwei Wang, and Shuli Chen. "Moving Object Detection Based on Background Compensation and Deep Learning." Symmetry 12, no. 12 (2020): 1965. http://dx.doi.org/10.3390/sym12121965.

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Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specific

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Krerngkamjornkit, Rapee, and Milan Simic. "Multi Object Detection and Tracking from Video File." Applied Mechanics and Materials 533 (February 2014): 218–25. http://dx.doi.org/10.4028/www.scientific.net/amm.533.218.

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This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results

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Miss., Aditi Kumbhar, and Pradip Bhaskar Dr. "A Review on Motion Detection Techniques." International Journal of Trend in Scientific Research and Development 2, no. 1 (2017): 736–40. https://doi.org/10.31142/ijtsrd5928.

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Motion detection is the process of detecting moving objects in background images. Motion detection plays a fundamental role in any object tracking or video surveillance algorithm. The reliability with which potential foreground objects in movement can be identified, directly impacts on the efficiency and performance level achievable by subsequent processing stages of tracking or object recognition. The system automatically performs a task and gives alert to security in an area. This paper represents review on Motion detection is an essential for many video applications such as video surveillan

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Yao, Mufeng, Jinlong Peng, Qingdong He, et al. "MM-Tracker: Motion Mamba for UAV-platform Multiple Object Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 9409–17. https://doi.org/10.1609/aaai.v39i9.33019.

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Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) platforms requires efficient motion modeling. This is because UAV-MOT faces both local object motion and global camera motion. Motion blur also increases the difficulty of detecting large moving objects. Previous UAV motion modeling approaches either focus only on local motion or ignore motion blurring effects, thus limiting their tracking performance and speed. To address these issues, we propose the Motion Mamba Module, which explores both local and global motion features through cross-correlation and bi-directional Mamba Modu

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Ramadhan, Dara Incam, Indah Permata Sari, and Linna Oktaviana Sari. "COMPARISON OF BACKGROUND SUBTRACTION, SOBEL, ADAPTIVE MOTION DETECTION, FRAME DIFFERENCES, AND ACCUMULATIVE DIFFERENCES IMAGES ON MOTION DETECTION." SINERGI 22, no. 1 (2018): 51. http://dx.doi.org/10.22441/sinergi.2018.1.009.

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Nowadays, digital image processing is not only used to recognize motionless objects, but also used to recognize motions objects on video. One use of moving object recognition on video is to detect motion, which implementation can be used on security cameras. Various methods used to detect motion have been developed so that in this research compared some motion detection methods, namely Background Substraction, Adaptive Motion Detection, Sobel, Frame Differences and Accumulative Differences Images (ADI). Each method has a different level of accuracy. In the background substraction method, the r

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Calabro, F. J., S. Soto-Faraco, and L. M. Vaina. "Acoustic facilitation of object movement detection during self-motion." Proceedings of the Royal Society B: Biological Sciences 278, no. 1719 (2011): 2840–47. http://dx.doi.org/10.1098/rspb.2010.2757.

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In humans, as well as most animal species, perception of object motion is critical to successful interaction with the surrounding environment. Yet, as the observer also moves, the retinal projections of the various motion components add to each other and extracting accurate object motion becomes computationally challenging. Recent psychophysical studies have demonstrated that observers use a flow-parsing mechanism to estimate and subtract self-motion from the optic flow field. We investigated whether concurrent acoustic cues for motion can facilitate visual flow parsing, thereby enhancing the

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Ashwini, Deshmukh, Gupta Jahanvi, Tulsulkar Bhagyashree, Sagvekar Siddhali, and Patil Geetanjali. "Object Detection and Motion Tracking." Recent Trends in Information Technology and its Application 3, no. 1 (2020): 1–5. https://doi.org/10.5281/zenodo.3695108.

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<em>Tracking, learning and detection in the real video is very important for video surveillance. In this paper we proposed the object detection method.In this project we have implemented deep learning algorithm which uses OpenCV framework. Output is depends on complex shapes, rapid motion and illumination changes.</em>

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Zhang, Xueyang, Junhua Xiang, and Yulin Zhang. "Space Object Detection in Video Satellite Images Using Motion Information." International Journal of Aerospace Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1024529.

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Compared to ground-based observation, space-based observation is an effective approach to catalog and monitor increasing space objects. In this paper, space object detection in a video satellite image with star image background is studied. A new detection algorithm using motion information is proposed, which includes not only the known satellite attitude motion information but also the unknown object motion information. The effect of satellite attitude motion on an image is analyzed quantitatively, which can be decomposed into translation and rotation. Considering the continuity of object moti

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Wang, Zhongli, Litong Fan, and Baigen Cai. "A 3D Relative-Motion Context Constraint-Based MAP Solution for Multiple-Object Tracking Problems." Sensors 18, no. 7 (2018): 2363. http://dx.doi.org/10.3390/s18072363.

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Multi-object tracking (MOT), especially by using a moving monocular camera, is a very challenging task in the field of visual object tracking. To tackle this problem, the traditional tracking-by-detection-based method is heavily dependent on detection results. Occlusion and mis-detections will often lead to tracklets or drifting. In this paper, the tasks of MOT and camera motion estimation are formulated as finding a maximum a posteriori (MAP) solution of joint probability and synchronously solved in a unified framework. To improve performance, we incorporate the three-dimensional (3D) relativ

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Ali Andre, Julfikar. "SISTEM SECURITY WEBCAM DENGAN MENGGUNAKAN MICROSOFT VISUAL BASIC (6.0)." Rabit : Jurnal Teknologi dan Sistem Informasi Univrab 1, no. 2 (2016): 46–58. http://dx.doi.org/10.36341/rabit.v1i2.23.

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Webcam (Web Camera) is a designation for real-time cameras (meaning the current situation) whose images can be accessed or viewed through the World Wide Web, instant messaging programs, or video call applications. During this time, the webcam application is only used to record and display objects, but has never been used for other applications such as security applications that are used to detect object movements. Webcams can not provide information about the motion of an object, passive or active objects. Therefore, research to improve the system to be able to detect motion from images captur

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Wang, Bo, Jinghong Liu, Shengjie Zhu, Fang Xu, and Chenglong Liu. "A Dual-Input Moving Object Detection Method in Remote Sensing Image Sequences via Temporal Semantics." Remote Sensing 15, no. 9 (2023): 2230. http://dx.doi.org/10.3390/rs15092230.

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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

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Paramanantham, Vinsent, and Dr SureshKumar S. "Multi View Video Summarization Using RNN and SURF Based High Level Moving Object Feature Frames." International Journal of Engineering Research in Computer Science and Engineering 9, no. 5 (2022): 1–14. http://dx.doi.org/10.36647/ijercse/09.05.art001.

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Multi-View Video summarization is a process to ease the storage consumption that facilitates organized storage, and perform other mainline videos analytical task. This in-turn helps quick search or browse and retrieve the video data with minimum time and without losing crucial data. In static video summarization, there is less challenge in time and sequence issues to rearrange the video-synopsis. The low-level features are easy to compute and retrieve. But for high-level features like event detection, emotion detection, object recognition, face detection, gesture detection, and others requires

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Madhan, K., and N. Shanmugapriya. "Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos." Indian Journal of Information Sources and Services 14, no. 1 (2024): 9–16. http://dx.doi.org/10.51983/ijiss-2024.14.1.3895.

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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 prepr

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Chandrakar, Rupali. "A Study of YOLO-Based Object Detection for Visually Impaired Individuals." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 296–302. http://dx.doi.org/10.22214/ijraset.2024.58810.

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Abstract: An essential area of investigation involves recognizing and tracking objects, given the frequent changes in object motion, scene dimensions, occlusions, appearance, ego-motion, and lighting variations. Effective object tracking heavily relies on feature selection due to its critical role. This process is integral to numerous real-time applications like vehicle detection and video surveillance. To address detection challenges, tracking methods need to be adept at handling object movement and appearance changes. Among these methods, tracking algorithms play a pivotal role in smoothing

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Li, Xueyong, Kai Zhao, Changhou Lu, and Yonghui Wang. "Quantitative motion detection of in-hand objects for robotic grasp manipulation." International Journal of Advanced Robotic Systems 16, no. 3 (2019): 172988141984633. http://dx.doi.org/10.1177/1729881419846336.

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In an actual grasp operation, grasp accidents (slant, rolling, turning over, and dropping) of in-hand objects occur frequently. Quantitative motion detection of in-hand objects is critical to optimize the grasp configuration and to improve the stability and dexterity of a grasp manipulation. In this article, an innovative method for quantitative measurement of the motions of in-hand objects is presented. Firstly, the slip information at object–finger interface between adjacent states is detected by three omnidirectional slip sensors; next, singular value decomposition method is applied to calc

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Roudaia, Eugenie, Finnegan J. Calabro, Lucia M. Vaina, and Fiona N. Newell. "Aging Impairs Audiovisual Facilitation of Object Motion Within Self-Motion." Multisensory Research 31, no. 3-4 (2018): 251–72. http://dx.doi.org/10.1163/22134808-00002600.

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The presence of a moving sound has been shown to facilitate the detection of an independently moving visual target embedded among an array of identical moving objects simulating forward self-motion (Calabro et al., Proc. R. Soc. B, 2011). Given that the perception of object motion within self-motion declines with aging, we investigated whether older adults can also benefit from the presence of a congruent dynamic sound when detecting object motion within self-motion. Visual stimuli consisted of nine identical spheres randomly distributed inside a virtual rectangular prism. For 1 s, all the sph

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Mohammed, Aree Ali. "Efficient Motion Detection Algorithm in Video Sequences." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 3 (2014): 4329–34. http://dx.doi.org/10.24297/ijct.v13i3.2763.

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HumanÂ motionÂ analysisÂ concernsÂ the detection,Â trackingÂ andÂ recognitionÂ ofÂ peopleÂ behaviors,Â from imageÂ sequencesÂ involvingÂ humans. AÂ referenceÂ frameÂ isÂ initiallyÂ usedÂ andÂ consideredÂ asÂ backgroundÂ information. While a new object enters into the frame, the foreground information and background information are identified using the reference frame as background model.In this paper, an efficient algorithm is proposed for objects detection in real time video sequences. The method aims at tracking an object like (human) in motion using background subtraction technique. The tra

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Liu, Sheng, Yangqing Wang, Fengji Dai, and Jingxiang Yu. "Simultaneous 3D Motion Detection, Long-Term Tracking and Model Reconstruction for Multi-Objects." International Journal of Humanoid Robotics 16, no. 04 (2019): 1950017. http://dx.doi.org/10.1142/s0219843619500178.

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Motion detection and object tracking play important roles in unsupervised human–machine interaction systems. Nevertheless, the human–machine interaction would become invalid when the system fails to detect the scene objects correctly due to occlusion and limited field of view. Thus, robust long-term tracking of scene objects is vital. In this paper, we present a 3D motion detection and long-term tracking system with simultaneous 3D reconstruction of dynamic objects. In order to achieve the high precision motion detection, an optimization framework with a novel motion pose estimation energy fun

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IMIYA, ATSUSHI, TOMOKI UENO, and IRIS FERMIN. "SYMMETRY DETECTION BY RANDOM SAMPLING AND VOTING PROCESS FOR MOTION ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 01 (2003): 83–125. http://dx.doi.org/10.1142/s0218001403002216.

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Symmetry of an object on a plane and in a space is an important geometric feature for biology, chemistry, and the understanding of human perception of figures. We propose a randomized method for the detection of symmetry in planar polygons and polyhedrons without assuming the predetermination of the centroids of the objects. Using a voting process, which is the main concept of the Hough transform in image processing, we transform the geometric computation for symmetry detection which is usually based on graph theory and combinatorial optimization, to the peak detection problem in a voting spac

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Kim, HaeHwan, Ho-Woong Lee, JinSung Lee, Okhwan Bae, and Chung-Pyo Hong. "An Effective Motion-Tracking Scheme for Machine-Learning Applications in Noisy Videos." Applied Sciences 13, no. 5 (2023): 3338. http://dx.doi.org/10.3390/app13053338.

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Detecting and tracking objects of interest in videos is a technology that can be used in various applications. For example, identifying cell movements or mutations through videos obtained in real time can be useful information for decision making in the medical field. However, depending on the situation, the quality of the video may be below the expected level, and in this case, it may be difficult to check necessary information. To overcome this problem, we proposed a technique to effectively track objects by modifying the simplest color balance (SCB) technique. An optimal object detection me

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A.K. Noaman, Redwan, Mohd Alauddin Mohd Ali, Nasharuddin Zainal, and Faisal Saeed. "Human Detection Framework for Automated Surveillance Systems." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (2016): 877. http://dx.doi.org/10.11591/ijece.v6i2.9578.

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Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not s

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A.K. Noaman, Redwan, Mohd Alauddin Mohd Ali, Nasharuddin Zainal, and Faisal Saeed. "Human Detection Framework for Automated Surveillance Systems." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (2016): 877. http://dx.doi.org/10.11591/ijece.v6i2.pp877-886.

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Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not s

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Jothipriya, I., and K. Krishnaveni. "Motion Object Detection Using BGS Technique." International Journal of Computing Algorithm 5, no. 1 (2016): 1–4. http://dx.doi.org/10.20894/ijcoa.101.005.001.001.

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Mamta, A. Baitule* Prof. Mukund R. Joshi. "OBJECT DETECTION AND TRACKING ALGORITHM FOR LOW VISION VIDEO." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 661–65. https://doi.org/10.5281/zenodo.51857.

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We propose a general framework for Object Recognition into regions and objects. In this framework, the detection and recognition of objects proceed simultaneously with image segmentation in a competitive and cooperative manner .Videos are a collection of sequential images with a constant time interval. So video can provide more information about our object when scenarios are changing with respect to time. Therefore, manually handling videos are quite impossible. So we need an automated devise to process these videos. Object tracking is  a process of segmenting a region of interest from a

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Syahfaridzah, Aldina, Adelia Kartika Panggabean, and Nabila Ayu Ardiningsih. "MENDETEKSI SECARA OTOMATIS OBJEK GERAKAN BERDASARKAN GAUSSIAN MIXTURE MODEL MENGGUNAKAN APLIKASI MATLAB." METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi 6, no. 2 (2020): 19–23. http://dx.doi.org/10.46880/mtk.v6i2.242.

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Automatically detects moving objects on a pedestrian video, each object will be detected by counting objects that appear at the beginning of the video based on a number. In detecting this object, the model used is the Gaussian Mixture Model whose performance is very effective if applied to any area that occurs in an object motion detected in the video. In this study, the Gaussian Mixture Model is used to model the background colors of each pixel. To facilitate the detection of this object, the Matlab 2016 application is also used, this application helps facilitate detection based on the size t

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Saif, A. F. M. Saifuddin, Anton Satria Prabuwono, and Zainal Rasyid Mahayuddin. "Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/890619.

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Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to appl

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Rushton, S. K., and M. F. Bradshaw. "Object motion from structure: the detection of object motion by a moving observer." Journal of Vision 4, no. 8 (2004): 795. http://dx.doi.org/10.1167/4.8.795.

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Хламов, С. В., В. Е. Саваневич, А. Б. Брюховецкий та С. С. Орышич. "МЕТОД СТАТИСТИЧНОГО МОДЕЛЮВАННЯ ДОСЛІДЖЕННЯ ПОКАЗНИКІВ ЯКОСТІ ВИЯВЛЕННЯ БЛИЗЬКОНУЛЬОВОГО ВИДИМОГО РУХУ ДОСЛІДЖУВАНОГО ОБ'ЄКТА НА СЕРІЇ CCD-КАДРІВ". Radioelectronic and Computer Systems, № 2 (6 вересня 2019): 51–61. http://dx.doi.org/10.32620/reks.2016.2.08.

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The method of statistical modeling of research of quality indication of detection of the object’s close-to-zero apparent motion on the series of CCD-frames is developed. This method takes into account the main features of the formation of the measurement position of object and features of the various methods of detection near-zero apparent motion of object on a series of CCD-frames. Also partly results of research of quality indication of detection of objects with near-zero apparent motion is provided by developed statistical modeling method in this article.

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Zhang, Wenlong, Xiaoliang Sun, and Qifeng Yu. "Moving Object Detection under a Moving Camera via Background Orientation Reconstruction." Sensors 20, no. 11 (2020): 3103. http://dx.doi.org/10.3390/s20113103.

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Moving object detection under a moving camera is a challenging question, especially in a complex background. This paper proposes a background orientation field reconstruction method based on Poisson fusion for detecting moving objects under a moving camera. As enlightening by the optical flow orientation of a background is not dependent on the scene depth, this paper reconstructs the background orientation through Poisson fusion based on the modified gradient. Then, the motion saliency map is calculated by the difference between the original and the reconstructed orientation field. Based on th

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Ge, L., and L. Dou. "An image restoration method for motion-blurred objects." Journal of Physics: Conference Series 2478, no. 6 (2023): 062004. http://dx.doi.org/10.1088/1742-6596/2478/6/062004.

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Abstract In military applications, the motion of high-speed objects usually causes motion blur in images, which makes it difficult to detect objects. We propose an image restoration method based on the line features and the degradation process of motion-blurred images. By analysing these features, we can estimate the motion state of the target and restore the images. The images processed by this method can get better performance in tasks like object detection and object tracking.

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He, Zuojie, Kai Zhao, and Dan Zeng. "TLtrack: Combining Transformers and a Linear Model for Robust Multi-Object Tracking." AI 5, no. 3 (2024): 938–47. http://dx.doi.org/10.3390/ai5030047.

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Multi-object tracking (MOT) aims at estimating locations and identities of objects in videos. Many modern multiple-object tracking systems follow the tracking-by-detection paradigm, consisting of a detector followed by a method for associating detections into tracks. Tracking by associating detections through motion-based similarity heuristics is the basic way. Motion models aim at utilizing motion information to estimate future locations, playing an important role in enhancing the performance of association. Recently, a large-scale dataset, DanceTrack, where objects have uniform appearance an

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True, Julian, and Naimul Khan. "Motion Vector Extrapolation for Video Object Detection." Journal of Imaging 9, no. 7 (2023): 132. http://dx.doi.org/10.3390/jimaging9070132.

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Despite the continued successes of computationally efficient deep neural network architectures for video object detection, performance continually arrives at the great trilemma of speed versus accuracy versus computational resources (pick two). Current attempts to exploit temporal information in video data to overcome this trilemma are bottlenecked by the state of the art in object detection models. This work presents motion vector extrapolation (MOVEX), a technique which performs video object detection through the use of off-the-shelf object detectors alongside existing optical flow-based mot

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Eldefrawy, Mahmoud, Scott A. King, and Michael Starek. "Partial Scene Reconstruction for Close Range Photogrammetry Using Deep Learning Pipeline for Region Masking." Remote Sensing 14, no. 13 (2022): 3199. http://dx.doi.org/10.3390/rs14133199.

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3D reconstruction is a beneficial technique to generate 3D geometry of scenes or objects for various applications such as computer graphics, industrial construction, and civil engineering. There are several techniques to obtain the 3D geometry of an object. Close-range photogrammetry is an inexpensive, accessible approach to obtaining high-quality object reconstruction. However, state-of-the-art software systems need a stationary scene or a controlled environment (often a turntable setup with a black background), which can be a limiting factor for object scanning. This work presents a method t

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Liu, Zhiguo, Enzheng Zhang, Qian Ding, Weijie Liao, and Zixiang Wu. "An Improved Method for Enhancing the Accuracy and Speed of Dynamic Object Detection Based on YOLOv8s." Sensors 25, no. 1 (2024): 85. https://doi.org/10.3390/s25010085.

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Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of the YOLOv8s model in dynamic object tracking tasks, this paper proposes a method to enhance both detection precision and speed based on YOLOv8s architecture. Specifically, a Focused Linear Attention mechanism is introduced into the YOLOv8s backbone network to enhance dynamic object detection accuracy, while the Ghost module is incorporated into the

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Nityananda, Vivek, James O’Keeffe, Diana Umeton, Adam Simmons, and Jenny C. A. Read. "Second-order cues to figure motion enable object detection during prey capture by praying mantises." Proceedings of the National Academy of Sciences 116, no. 52 (2019): 27018–27. http://dx.doi.org/10.1073/pnas.1912310116.

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Detecting motion is essential for animals to perform a wide variety of functions. In order to do so, animals could exploit motion cues, including both first-order cues—such as luminance correlation over time—and second-order cues, by correlating higher-order visual statistics. Since first-order motion cues are typically sufficient for motion detection, it is unclear why sensitivity to second-order motion has evolved in animals, including insects. Here, we investigate the role of second-order motion in prey capture by praying mantises. We show that prey detection uses second-order motion cues t

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Bachmann, Alexander, and Thao Dang. "Improving motion-based object detection by incorporating object-specific knowledge." International Journal of Intelligent Information and Database Systems 2, no. 2 (2008): 258. http://dx.doi.org/10.1504/ijiids.2008.018258.

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Wang, Zhipeng, Jinshi Cui, Hongbin Zha, Masataka Kagesawa, Shintaro Ono, and Katsushi Ikeuchi. "Foreground Object Detection by Motion-based Grouping of Object Parts." International Journal of Intelligent Transportation Systems Research 12, no. 2 (2014): 70–82. http://dx.doi.org/10.1007/s13177-013-0074-8.

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RAHMAN, M. MUSHARUF. "ADVANCED SHOPLIFTING PREVENTION AND ALERT STSYEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33871.

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Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using YOLOv7, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A compre

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Yan, Bao Dong, and Ying Yu. "Study on Detection of Human Motion Using a RGB Color Space Shadow Method with Mechanics Properties." Advanced Materials Research 703 (June 2013): 304–7. http://dx.doi.org/10.4028/www.scientific.net/amr.703.304.

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The aim of human mechanics is to reveal the mechanics properties of human motion. Especially, the purpose of human motion detection is detecting the moving people from continuous image sequences, extracting human body segments and then getting motion feature. The paper presents a shadow detection algorithm based on covariance difference operator based RGB color space and discusses its mechanics properties. The presented algorithm includes four steps: object detection, suspected shadow detection, shadow detection and post processing. The presented algorithm of adaptive shadow detection threshol

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Wang, Hengsen, Chenglizhao Chen, Linfeng Li, and Chong Peng. "Video Saliency Object Detection with Motion Quality Compensation." Electronics 12, no. 7 (2023): 1618. http://dx.doi.org/10.3390/electronics12071618.

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Video saliency object detection is one of the classic research problems in computer vision, yet existing works rarely focus on the impact of input quality on model performance. As optical flow is a key input for video saliency detection models, its quality significantly affects model performance. Traditional optical flow models only calculate the optical flow between two consecutive video frames, ignoring the motion state of objects over a period of time, leading to low-quality optical flow and reduced performance of video saliency object detection models. Therefore, this paper proposes a new

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Zhang, Chen, Zhengyu Xia, and Joohee Kim. "Video Object Detection Using Event-Aware Convolutional Lstm and Object Relation Networks." Electronics 10, no. 16 (2021): 1918. http://dx.doi.org/10.3390/electronics10161918.

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Common video-based object detectors exploit temporal contextual information to improve the performance of object detection. However, detecting objects under challenging conditions has not been thoroughly studied yet. In this paper, we focus on improving the detection performance for challenging events such as aspect ratio change, occlusion, or large motion. To this end, we propose a video object detection network using event-aware ConvLSTM and object relation networks. Our proposed event-aware ConvLSTM is able to highlight the area where those challenging events take place. Compared with tradi

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Chen, Tao, and Shijian Lu. "Object-Level Motion Detection From Moving Cameras." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 11 (2017): 2333–43. http://dx.doi.org/10.1109/tcsvt.2016.2587387.

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Girelli, Massimo. "Collinear Motion Strengthens Local Context in Visual Detection." i-Perception 11, no. 5 (2020): 204166952096112. http://dx.doi.org/10.1177/2041669520961125.

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Detection of elongated objects in the visual scene can be improved by additional elements flanking the object on the collinear axis. This is the collinear context effect (CE) and is represented in the long-range horizontal connection plexus in V1. The aim of this study was to test whether the visual collinear motion can improve the CE. In the three experiments of this study, the flank was presented with different types of motion. In particular, the collinear motion aligned with the longitudinal axis of the to-be-detected object: toward or away from it, and the orthogonal motion with a directio

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Minh. "MOVING OBJECT DETECTION FROM VIDEO CAPTURED BY MOVING SURVEILLANCE CAMERA." Journal of Military Science and Technology, no. 71 (February 5, 2021): 139–45. http://dx.doi.org/10.54939/1859-1043.j.mst.71.2021.139-145.

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This paper presents an effective method for the detection of multiple moving objects from a video sequence captured by a moving surveillance camera. Moving object detection from a moving camera is difficult since camera motion and object motion are mixed. In the proposed method, we created a panoramic picture from a moving camera. After that, with each frame captured from this camera, we used the template matching method to found its place in the panoramic picture. Finally, using the image differencing method, we found out moving objects. Experimental results have shown that the proposed metho

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Gupta, Ms Pritee, and Dr Yashpal Singh. "Implementation of Dynamic Threshold Method for Human Motion Detection in Video surveillance application." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 8 (2014): 4776–81. http://dx.doi.org/10.24297/ijct.v13i8.7077.

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Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. This paper implemented a method to detect moving object based on background subtraction. First of all, we establish a reliable background updating model based on statistical and use a dynamic optimi

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Chang, Yongxin, Huapeng Yu, Zhiyong Xu, Jing Zhang, and Chunming Gao. "Accurate Object Recognition with Assembling Appearance and Motion Information." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/195941.

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How to effectively detect object and accurately give out its visible parts is a major challenge for object detection. In this paper we propose an explicit occlusion model through integrating appearance and motion information. The model combines together two parts: part-level object detection with single frame and object occlusion estimation with continuous frames. It breaks through the performance bottleneck caused by lack of information and effectively improves object detection rate under severe occlusion. Through reevaluating the semantic parts, the detecting performance of partial object de

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Ye, Yangqing, Xiaolon Ma, Xuanyi Zhou, Guanjun Bao, Weiwei Wan, and Shibo Cai. "Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots." Sensors 23, no. 23 (2023): 9482. http://dx.doi.org/10.3390/s23239482.

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Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motion states usually contain varying degrees of blurriness, presenting a significant challenge for object detection. In particular, daily life scenes contain small objects like fruits and tableware, which are often occluded, further complicating object recognition and positioning. A dynamic and real-time object detection algorithm is proposed for home

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Liu, Jing-Sin, Wen-Hua Pan, Wen-Yang Ku, Y. H. Tsao, and Y. Z. Chang. "Simulation-based fast collision detection for scaled polyhedral objects in motion by exploiting analytical contact equations." Robotica 34, no. 1 (2014): 118–34. http://dx.doi.org/10.1017/s0263574714000939.

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SUMMARYBased on the results of the study of convex object motion1 (J. Hopcroft and G. Wilfong, “Motion of objects in contact,” Int. J. Robot. Res., 4(4), 32–46 (1986)), this paper addresses the problem of exact collision detection of a pair of scaled convex polyhedra in relative motion, and determines the contact conditions of tangential contact features, arbitrary relative motion involving translation and rotation, and uniform scaling of the objects about a fixed point. We propose a new concept of the decision curve based on analytical contact equations that characterize a continuum of scalin

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