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GAMA: Geometric analysis based motion-aware architecture for moving object segmentation

COMPUTER VISION AND IMAGE UNDERSTANDING 2023年第1期234卷

作者： Xu, Bangwu Wu, Qin Chai, Zhilei Guo, Xueliang Shi, Jianbo Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Peoples R China Uisee Technol Co Ltd Shanghai Peoples R China Univ Penn GRASP Lab Philadelphia PA USA

moving object segmentation in real-world scenes is of critical significance for many computer vision applications. However, there are many challenges in moving object segmentation. It is difficult to distinguish objects with motion degeneracy. Besides, complex scenes and noisy 2D optical flows also effect the result of moving object segmentation. In this paper, to address difficulties caused by motion degeneracy, we analyze the classic motion degeneracy from a new geometric perspective. To identify objects with motion degeneracy, we propose a reprojection cost and an optical flow contrast cost which are fed into the network to enrich motion features. Furthermore, a novel geometric constraint called bidirectional motion constraint is proposed to detect moving objects with weak motion features. In order to tackle more complex scenes, we also introduce a motion aware architecture to predict instance masks of moving objects. Extensive experiments are conducted on the KITTI dataset, the JNU-UISEE dataset and the KittiMoSeg dataset, and our proposed method achieves excellent performance.

关键词： moving object segmentation Motion degeneracy Geometric analysis Bidirectional motion constraint Motion-aware architecture

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An Intelligent moving object segmentation Using Hybrid IFCM-CSS Clustering Model

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2024年第8期38卷

作者： Veera Raghavulu, Vivaram Prasad, Ande Comp Sci & Engn NBKR Inst Sci & Technol Vijayanagar 524413 Andhra Prades India Vikrama Simhapuri Univ Comp Sci & Engn Kakutur Nellore 524324 Andhra Prades India

In this study, a novel hybrid deep clustering approach is proposed for the effective moving object segmentation. Initially, the data is collected, and the keyframe selection is performed using the threshold-based Kennard-Stone method. Then, the preprocessing step involves noise filtering using bilateral wavelet thresholding and binary color conversion. The blob detection is performed using normalized Laplacian of Gaussian. Finally, the segmentation of moving objects is performed using a hybrid clustering approach called improved fuzzy C-mean (IFCM) clustering with chaotic salp swarm (CSS) optimization algorithm (Hybrid IFCM-CSS). The overall evaluation is done in MATLAB. The performance of the hybrid IFCM-CSS is compared to other approaches based on some measures. The proposed Hybrid IFCM-CSS achieves the highest precision of 0.971, using the SBM-RGBD dataset.

关键词： moving object segmentation frames video analysis deep clustering chaotic salp swarm

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Improvement of moving object segmentation accuracy in video sequences based on adaptive symmetrical difference and background subtraction

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OPTOELECTRONICS AND ADVANCED MATERIALS-RAPID COMMUNICATIONS 2010年第12期4卷 2172-2177页

作者： Li, J. Xu, J. Sang, X. Wang, Y. Yan, B. Yu, C. Beijing Univ Posts & Telecommun Minist Educ Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China China Civil Airport Equipment Co Ltd Beijing 100621 Peoples R China Minist Publ Secur Inst Forens Sci Beijing 100038 Peoples R China

The segmentation accuracy of moving object in video sequences is usually influenced by illumination variation, noise, shadow, and ghost after the initially static background objects were removed. A novel algorithm combining adaptive symmetrical difference with background subtraction is proposed. The moving object mask is detected through the adaptive symmetrical difference, and points between any pairs on the contour of the mask as motion region are determined. And then, the adaptive background subtraction is carried out in the acquired region and points as motion region are labeled to extract accurate moving object. Morphological operation and shadow cancellation are adopted to refine the result. Experimental results show that the algorithm is robust and effective in improving the segmentation accuracy.

关键词： moving object segmentation Symmetrical difference Background subtraction Adaptive threshold

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A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in moving object segmentation

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SENSORS 2023年第14期23卷 6492页

作者： Jiang, Rui Li, Jiatao Bu, Weifeng Shen, Xiang Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

Model evaluation is critical in deep learning. However, the traditional model evaluation approach is susceptible to issues of untrustworthiness, including insecure data and model sharing, insecure model training, incorrect model evaluation, centralized model evaluation, and evaluation results that can be tampered easily. To minimize these untrustworthiness issues, this paper proposes a blockchain-based model evaluation framework. The framework consists of an access control layer, a storage layer, a model training layer, and a model evaluation layer. The access control layer facilitates secure resource sharing. To achieve fine-grained and flexible access control, an attribute-based access control model combining the idea of a role-based access control model is adopted. A smart contract is designed to manage the access control policies stored in the blockchain ledger. The storage layer ensures efficient and secure storage of resources. Resource files are stored in the IPFS, with the encrypted results of their index addresses recorded in the blockchain ledger. Another smart contract is designed to achieve decentralized and efficient management of resource records. The model training layer performs training on users' servers, and, to ensure security, the training data must have records in the blockchain. The model evaluation layer utilizes the recorded data to evaluate the recorded models. A method in the smart contract of the storage layer is designed to enable evaluation, with scores automatically uploaded as a resource attribute. The proposed framework is applied to deep learning-based motion object segmentation, demonstrating its key functionalities. Furthermore, we validated the storage strategy adopted by the framework, and the trustworthiness of the framework is also analyzed.

关键词： blockchain deep learning model evaluation computer vision moving object segmentation

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Accurate moving object segmentation in unconstraint videos based on robust seed pixels selection

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第4期17卷

作者： Zhang, Wenlong Sun, Xiaoliang Yu, Qifeng Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Hunan Prov Key Lab Image Measurement & Vis Nav Changsha 410073 Peoples R China

Due to the clutter background motion, accurate moving object segmentation in unconstrained videos remains a significant open problem, especially for the slow-moving object. This article proposes an accurate moving object segmentation method based on robust seed selection. The seed pixels of the object and background are selected robustly by using the optical flow cues. Firstly, this article detects the moving object's rough contour according to the local difference in the weighted orientation cues of the optical flow. Then, the detected rough contour is used to guide the object and the background seed pixel selection. The object seed pixels in the previous frame are propagated to the current frame according to the optical flow to improve the robustness of the seed selection. Finally, we adopt the random walker algorithm to segment the moving object accurately according to the selected seed pixels. Experiments on publicly available data sets indicate that the proposed method shows excellent performance in segmenting moving objects accurately in unconstraint videos.

关键词： moving object segmentation local difference seed pixels selection random walker

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Real-Time LiDAR Point-Cloud moving object segmentation for Autonomous Driving

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SENSORS 2023年第1期23卷 547-547页

作者： Xie, Xing Wei, Haowen Yang, Yongjie Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Columbia Univ Dept Comp Sci New York NY 10027 USA

The key to autonomous navigation in unmanned systems is the ability to recognize static and moving objects in the environment and to support the task of predicting the future state of the environment, avoiding collisions, and planning. However, because the existing 3D LiDAR point-cloud moving object segmentation (MOS) convolutional neural network (CNN) models are very complex and have large computation burden, it is difficult to perform real-time processing on embedded platforms. In this paper, we propose a lightweight MOS network structure based on LiDAR point-cloud sequence range images with only 2.3 M parameters, which is 66% less than the state-of-the-art network. When running on RTX 3090 GPU, the processing time is 35.82 ms per frame and it achieves an intersection-over-union(IoU) score of 51.3% on the SemanticKITTI dataset. In addition, the proposed CNN successfully runs the FPGA platform using an NVDLA-like hardware architecture, and the system achieves efficient and accurate moving-object segmentation of LiDAR point clouds at a speed of 32 fps, meeting the real-time requirements of autonomous vehicles.

关键词： moving object segmentation LiDAR CNN FPGA

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Multi-sensor fusion for robust localization with moving object segmentation in complex dynamic 3D scenes

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2023年 124卷

作者： Li, Qipeng Zhuang, Yuan Huai, Jianzhu Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China Wuhan Inst Quantum Technol Wuhan 430010 Hubei Peoples R China

Accurate and robust localization is one of the key modules of autonomous driving and multi-sensor robotic systems in complex urban scenes. Compared with camera-based localization, which is sensitive to the influence of light changes caused by different environments, LiDAR-based localization has higher accuracy and robustness. However, the existing LiDAR localization often assumes that the scene is static and ignores the impact of moving objects on localization and mapping. At present, globally consistent multi-sensor localization is an open issue, especially in the complex dynamic environment. We propose a LiDAR-based dynamic scene perception and multi-sensor localization framework. To improve the localization robustness in dynamic complex scenes, we design a moving object segmentation (MOS) module based on spatio-temporal information, and further optimize ground segmentation. On this basis, we design a lightweight, tightly-coupled LiDAR-IMU-GNSS odometry framework, which achieves accurate and real-time position estimation. In the front-end, based on the iterative Extended Kalman filter, we directly register the original 3D point cloud to the map without a feature extractor, which can adapt to different LiDAR scanning patterns, and then can be tightly-coupled with IMU data. Compared with the existing methods, we verify the proposed MOS performance on KITTI datasets, and our approach achieves state-of-the-art performance. In addition, we demonstrate the localization performance of the proposed framework on two public datasets and one solid-state LiDAR dataset. The results show that our system is more accurate than the existing advanced fusion-based localization method.

关键词： LiDAR localization Multi-sensor fusion moving object segmentation Deep learning Autonomous driving

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A Survey of Efficient Deep Learning Models for moving object segmentation

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APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING 2023年第1期12卷

作者： Hou, Bingxin Liu, Ying Ling, Nam Ren, Yongxiong Liu, Lingzhi Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA Kwai Inc Palo Alto CA 94306 USA

moving object segmentation (MOS) is the process of identifying dynamic objects from video frames, such as moving vehicles or pedestrians, while discarding the background. It plays an essential role in many real-world applications such as autonomous driving, mobile robots, and surveillance systems. With the availability of a huge amount of data and the development of powerful computing infrastructure, deep learning-based methods have shown remarkable improvements in MOS tasks. However, as the dimension of data becomes higher and the network architecture becomes more complicated, deep learning-based MOS models are computationally intensive, which limits their deployment on resource-constrained devices and in delay-sensitive applications. Therefore, more research started to develop fast and lightweight models. This paper aims to provide a comprehensive review of deep learning-based MOS models, with a focus on efficient model design techniques. We summarize a variety of MOS datasets, and conduct a thorough review of segmentation accuracy metrics and model efficiency metrics. Most importantly, we compare the performance of efficient MOS models on popular datasets, identify competitive models and analyze their essential techniques. Finally, we point out existing challenges and present future research directions.

关键词： Background subtraction change detection deep learning efficient model design lightweight model moving object segmentation real-time processing video object segmentation.

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Unsupervised moving object segmentation from Stationary or moving Camera Based on Multi-frame Homography Constraints

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SENSORS 2019年第19期19卷 4344页

作者： Cui, Zhigao Jiang, Ke Wang, Tao Xian Res Inst High Tech Xian 710025 Shaanxi Peoples R China

moving object segmentation is the most fundamental task for many vision-based applications. In the past decade, it has been performed on the stationary camera, or moving camera, respectively. In this paper, we show that the moving object segmentation can be addressed in a unified framework for both type of cameras. The proposed method consists of two stages: (1) In the first stage, a novel multi-frame homography model is generated to describe the background motion. Then, the inliers and outliers of that model are classified as background trajectories and moving object trajectories by the designed cumulative acknowledgment strategy. (2) In the second stage, a super-pixel-based Markov Random Fields model is used to refine the spatial accuracy of initial segmentation and obtain final pixel level labeling, which has integrated trajectory classification information, a dynamic appearance model, and spatial temporal cues. The proposed method overcomes the limitations of existing object segmentation algorithms and resolves the difference between stationary and moving cameras. The algorithm is tested on several challenging open datasets. Experiments show that the proposed method presents significant performance improvement over state-of-the-art techniques quantitatively and qualitatively.

关键词： moving object segmentation motion trajectory multi-frame homography constraint Markov random fields model

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Fast moving object segmentation Based On Active Contours

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JOURNAL OF COMPUTERS 2012年第4期7卷 863-869页

作者： Zhang, Hui-min Duan, Zhi-yong Zhu, Zhong-jie Wang, Yuer Zhengzhou Univ Phys Engn Coll Zhengzhou 450001 Henan Peoples R China Zhejiang Wanli Univ Ningbo Key Lab DSP Ningbo 315100 Zhejiang Peoples R China

Active contour method is widely used in the image processing field. Recently, it has been used in object segmentation and has attracted great attention. However, most of the existing object segmentation methods based on active contours are complex and time-consuming. They cannot be used in some real-time applications. Hence, in this paper, a fast and efficient moving object segmentation algorithm based on an improved active contour model is proposed. Firstly, moving regions are detected based on frame differences. Then their boundaries are extracted and used as the coarse objects' contours for the next segmentation based on active contour model. Thirdly, an improved active contour method is introduced to extract the final objects with the coarse objects' contours as initial values. Experiments are implemented and the results show that the proposed algorithm can segment and track moving targets quickly and correctly.

关键词： moving object segmentation frame difference active contour model fast algorithm

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