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Conference on Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications X

作者： He, Qiang Chu, Chee-Hung Henry Camargo, Aldo Mississippi Valley State Univ Dept Math Comp & Informat Sci Itta Bena MS 38941 USA Univ Louisiana Lafayette Ctr Adv Comp Studies Lafayette LA 70504 USA Res & Dev I T Lima Peru

ISBN: (纸本)9780819495044

Automatic object detection and tracking has been widely applied in the video surveillance systems for homeland security and data fusion in the remote sensing and airborne imagery. The typical applications include human motion analysis, vehicle detection, and architectural building detection. Here we conduct object detection and tracking under planar constraints for interesting objects. Planar surface abounds in man-made environment. It provides much useful information for image understanding and then can be adopted to improve the performance of object detection and tracking. The experiments on real data show that object detection and tracking could be successfully implemented by incorporating planar information of interesting objects.

关键词： object detection and tracking SIFT Features Planar Homography Singular Value Decomposition Inverse Power Method

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object detection and tracking FOR NIGHT SURVEILLANCE BASED ON SALIENT CONTRAST ANALYSIS

OBJECT DETECTION AND TRACKING FOR NIGHT SURVEILLANCE BASED O...

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16th IEEE International Conference on Image Processing

作者： Wang, Liangsheng Huang, Kaiqi Huang, Yongzhen Tan, Tieniu Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781424456536

Night surveillance is a challenging task because of low brightness, low contrast, low Signal to Noise Ratio (SNR) and low appearance information. Most existing models for night surveillance share the following problems: a lack of adaptability for different scenes and separation between detection and tracking. To solve these problems we propose a model based on Salient Contrast Change (SCC) feature, which applies learning process to enhance adaptability and analyzes trajectories to improve the effectiveness of detection. Empirical studies on several real night videos show that the proposed model is more effective than the original CC model and other traditional models.

关键词： Video surveillance object detection and tracking trajectory analysis Salient Contrast Analysis

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object detection and tracking - A Survey 7

Object Detection and Tracking - A Survey

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7th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Reddy, K. Rasool Priya, K. Hari Neelima, N. Malla Reddy Inst Technol & Sci Secunderabad India

ISBN: (纸本)9781509000760

object tracking is one of the major fundamental challenging problems in computer vision applications due to difficulties in tracking of objects can arises due to intrinsic and extrinsic factors like deformation, camera motion, motion blur and occlusion. This paper proposes a literature review on several state-of-the-art object detection and tracking algorithms in order to reduce the tracking drift.

关键词： Computer vision object detection and tracking image processing

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HYDRO-3D: Hybrid object detection and tracking for Cooperative Perception Using 3D LiDAR

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第8期8卷 4069-4080页

作者： Meng, Zonglin Xia, Xin Xu, Runsheng Liu, Wei Ma, Jiaqi Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA 90095 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

3D-LiDAR-based cooperative perception has been generating significant interest for its ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues that can be problematic for single-vehicle perception. Despite its effectiveness in overcoming various challenges, cooperative perception's performance can still be affected by the aforementioned issues when Connected Automated Vehicles (CAVs) operate at the edges of their sensing range. Our proposed approach called HYDRO-3D aims to improve object detection performance by explicitly incorporating historical object tracking information. Specifically, HYDRO-3D combines object detection features from a state-of-the-art object detection algorithm (V2X-ViT) with historical information from the object tracking algorithm to infer objects. Afterward, a novel spatial-temporal 3D neural network performing global and local manipulations of object-tracking historical data is applied to generate the feature map to enhance object detection. The proposed HYDRO-3D method is comprehensively evaluated on the state-of-the-art V2XSet. The qualitative and quantitative experiment results demonstrate that the HYDRO-3D can effectively utilize the object tracking information and achieve robust object detection performance. It outperforms the SOTA V2X-ViT by 3.7% in AP@0.7 of object detection for CAVs and can also be generalized to single-vehicle object detection with 4.5% improvement in AP@0.7.

关键词： Cooperative driving automation cooperative perception object detection and tracking LiDAR

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PAE: Portable Appearance Extension for Multiple object detection and tracking in Traffic Scenes

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IEEE ACCESS 2022年 10卷 37257-37268页

作者： Mohamed, Ibrahim Soliman Chuan, Lim Kim Univ Teknikal Malaysia Melaka Fac Elect & Comp Engn Ctr Telecommun Res & Innovat Machine Learning & Signal Proc Durian Tunggal 76100 Malaysia

Multi-object tracking (MOT) is an important field in computer vision that provides a critical understanding of video analysis in various applications, such as vehicle tracking in intelligent transportation systems (ITS). Several deep learning-based approaches have been introduced to basic motion and IoU trackers by extracting appearance features to assist in challenging situations such as lossy detection and occlusion. This study proposes a portable appearance extension (PAE) for single-stage object detection to jointly detect and extract appearance embeddings using a shared model. Furthermore, a novel training framework with a single image and without re-identification annotations is presented using an augmentation module, saving a tremendous amount of human labeling effort and increasing the real-world application adoption rate. Using UA-DETRAC dataset, RetinaNet-PAE and SSD-PAE achieve comparable results with current state-of-the-art models, where RetinaNet-PAE prioritizes detection and tracking performance with a 58.0% HOTA score and 4 FPS. In contrast, SSD-PAE prioritizes latency performance with a 47.3% HOTA score and 40 FPS.

关键词： Feature extraction object detection Detectors Training Computational modeling Computer architecture Task analysis Computer vision object detection and tracking appearance embedding vehicle tracking

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Granulated RCNN and Multi-Class Deep SORT for Multi-object detection and tracking

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2022年第1期6卷 171-181页

作者： Pramanik, Anima Pal, Sankar K. Maiti, J. Mitra, Pabitra Indian Inst Technol Kharagpur Ind & Syst Engn Kharagpur 721302 W Bengal India ISI Soft Comp Kolkata 700108 India IIT Kharagpur Comp Sci & Engn Kharagpur 721302 W Bengal India

In this article, two new models, namely granulated RCNN (G-RCNN) and multi-class deep SORT (MCD-SORT), for object detection and tracking, respectively from videos are developed. object detection has two stages: object localization (region of interest RoI) and classification. G-RCNN is an improved version of the well-known Fast RCNN and Faster RCNN for extracting RoIs by incorporating the unique concept of granulation in a deep convolutional neural network. Granulation with spatio-temporal information enables more accurate extraction of RoIs (object regions) in unsupervised mode. Compared to Fast and Faster RCNNs, G-RCNN uses (i) granules (clusters) formed over the pooling feature map, instead of its all feature values, in defining RoIs, (ii) only the positive RoIs during training, instead of the whole RoI-map, (iii) videos directly as input, rather than static images, and (iv) only the objects in RoIs, instead of the entire feature map, for performing object classification. All these lead to the improvement in real-time detection speed and accuracy. MCD-SORT is an advanced form of the popular Deep SORT. In MCD-SORT, the searching for association of objects with trajectories is restricted only within the same categories. This increases the performance in multi-class tracking. These characteristic features have been demonstrated over 37 videos containing single-class, two-class, and multi-class objects. Superiority of the models over several state-of-the-art methodologies is also established extensively, both qualitatively and quantitatively.

关键词： Videos Detectors Feature extraction Proposals Computational modeling Steel object detection Deep CNN Foreground region proposal Granulation object detection and tracking Video analysis

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Soft Computing based object detection and tracking approaches: State-of-the-Art survey

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APPLIED SOFT COMPUTING 2018年 70卷 423-464页

作者： Kaushal, Manisha Khehra, Baljit S. Sharma, Akashdeep Panjab Univ UIET Chandigarh India BBSBEC Comp Sci Engn Fatehgarhsahib Punjab India

In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. detection and tracking includes extraction of moving object from frames and continuous tracking it thereafter forming persistent object trajectories over time. There are some really smart techniques proposed by researchers for efficient and robust detection or tracking of objects in videos. A comprehensive coverage of such innovative techniques for which solutions have been motivated by theories of soft computing approaches is proposed. The main objective of this research investigation is to study and highlight efforts of researchers who had conducted some brilliant work on soft computing based detection and tracking approaches in video sequence. The study is novel as it traces rise of soft computing methods in field of object detection and tracking in videos which has been neglected over the years. The survey is compilation of studies on neural network, deep learning, fuzzy logic, evolutionary algorithms, hybrid and recent innovative approaches that have been applied to field of detection and tracking. The paper also highlights benchmark datasets available to researchers for experimentation and validation of their own algorithms. Major research challenges in the field of detection and tracking along with some recommendations are also provided. The paper provides number of analyses to guide future directions of research and advocates for more applications of soft computing approaches for object detection and tracking approaches in videos. The paper is targeted at young researchers who will like to see it as platform for introduction to a mature and relatively complex field. The study will be helpful in appropriate use of an existing method for systematically designing a new approach or improving performance of existing approaches. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Soft computing object detection and tracking Neural network and deep learning Fuzzy logic Evolutionary algorithms

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Bubble feature extraction in subcooled flow boiling using AI-based object detection and tracking techniques

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2024年 222卷

作者： Zhou, Wen Miwa, Shuichiro Tsujimura, Ryoma Nguyen, Thanh-Binh Okawa, Tomio Okamoto, Koji Univ Tokyo Sch Engn Dept Nucl Engn & Management 7-3-1 HongoBunkyo Ku Tokyo 1138654 Japan Univ Electrocommun Dept Mech & Intelligent Syst Engn 1-5-1Chofugaoka Chofu Tokyo 1828585 Japan

Subcooled flow boiling is a pivotal process prevalent in a myriad of scientific investigations and engineering applications, particularly in the realm of heat transfer system design and the foundational study of phase transition dynamics. The life cycle of bubbles, from nucleation and growth to departure and coalescence, along with their interaction with heat and mass transfer processes, critically influence the overall heat transfer efficiency. Nonetheless, the drastic transformations that bubbles undergo from inception to disappearance in subcooled flow boiling pose significant challenges for conventional bubble detection methods, particularly concerning condensing bubbles. In light of this, a cutting-edge AI-based method for condensing bubble detection and tracking in subcooled flow boiling is developed and validated in the present study. The present approach first identifies bubbles using object detection technique and subsequently tracks them across sequential frames. The proposed method demonstrates a robust capability of detecting approximately 90% of condensing bubbles within subcooled flow boiling. Furthermore, key thermal-hydraulic parameters in subcooled flow boiling such as aspect ratio, Sauter mean diameter, departure diameter, growth time, and bubble lifetime, were successfully extracted using the proposed AI-based model. Its results are compared with empirical correlations, and show a commendable consistency, demonstrating the viability and accuracy of the advanced AI-based model in analyzing the complex dynamics of subcooled flow boiling. The advantage of the newly developed method is preliminarily verified in the present study, and further validation is underway to corroborate its boarded application.

关键词： Condensation bubble Subcooled flow boiling Feature extraction object detection and tracking

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An Energy-Efficient Reconfigurable AI-Based object detection and tracking Processor Supporting Online object Learning

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IEEE SOLID-STATE CIRCUITS LETTERS 2022年 5卷 78-81页

作者： Gong, Yuchuan Zhang, Teng Guo, Hongtao Liu, Qingsong Que, Luying Jia, Conghan Huang, Jiahui Liu, Ye Gan, Jiayan Xie, Yuxiang Zhou, Yong Liu, Lili Xiang, Xiaoqiang Chang, Liang Yan, Rui Zhou, Jun Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Zhejiang Univ Technol Coll Comp Sci Hangzhou 310014 Peoples R China

This letter presents an energy-efficient reconfigurable AI-based object detection and tracking processor for smart drone/robot applications. Several techniques have been proposed to achieve high energy efficiency while supporting flexible object detection and tracking tasks with online object learning, including a reconfigurable object detection and tracking architecture with reconfigurable neural network (NN) engine, an online object learning architecture with shared NN inference and learning engine and automatic label generation engine, and a layer- and stride-aware NN computing technique. Compared with several state-of-the-art designs, the proposed design achieves better energy efficiency (2.13 mJ/frame), while supporting flexible object detection and tracking tasks with online object learning.

关键词： Random access memory object detection Engines Computer architecture Artificial neural networks Control systems Micromechanical devices Energy efficient neural network (NN) object detection and tracking processor reconfigurable

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Infrastructure-Based object detection and tracking for Cooperative Driving Automation: A Survey

Infrastructure-Based Object Detection and Tracking for Coope...

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33rd IEEE Intelligent Vehicles Symposium (IEEE IV)

作者： Bai, Zhengwei Wu, Guoyuan Qi, Xuewei Liu, Yongkang Oguchi, Kentaro Barth, Matthew J. Univ Calif Riverside Ctr Environm Res & Technol Riverside CA 92507 USA Toyota Motor North Amer InfoTech Labs Mountain View CA 94043 USA

ISBN: (纸本)9781665488211

object detection and tracking play a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems. Although current computer vision technologies can provide satisfactory object detection results in occlusion-free scenarios, the perception performance of onboard sensors is inevitably limited by the range and occlusion. Owing to the flexible location and pose for sensor installation, infrastructure-based detection, and tracking systems can enhance the perception capability of connected vehicles;as such, they have quickly become a popular research topic. In this survey paper, we review the research progress for infrastructure-based object detection and tracking systems. Architectures of roadside perception systems based on different types of sensors are reviewed to show a high-level description of the workflows for infrastructure-based perception systems. Roadside sensors and different perception methodologies are reviewed and analyzed with detailed literature to provide a low-level explanation for specific methods followed by Datasets and Simulators to draw an overall landscape of infrastructure-based object detection and tracking methods. We highlight current opportunities, open problems, and anticipated future trends.

关键词： Roadside Sensor object detection and tracking Cooperative Driving Automation Cooperative Perception

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