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Multi-camera People Tracking With Mixture of Realistic and Synthetic Knowledge

Multi-camera People Tracking With Mixture of Realistic and S...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Nguyen, Quang Qui-Vinh Le, Huy Dinh-Anh Chau, Truc Thi-Thanh Luu, Duc Trung Chung, Nhat Minh Ha, Synh Viet-Uyen International University School of Computer Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350302493

This paper presents a solution for Track 1 of the AI City Challenge 2023, which involves Multi-Camera People Tracking in indoor scenarios. The proposed framework comprises four modules: Vehicle detection, ReID feature extraction, single-camera multi-target tracking (SCMT), single-camera matching, and multi-camera matching. A significant contribution of our approach is the introduction of ID switch detection and ID switch splitting using the Gaussian mixture model, which efficiently addresses the problem of tracklets with ID switches. Furthermore, our system performs well in matching both synthetic and real data. The proposed R-matching algorithm performs exceptionally well in real scenarios despite being trained on synthetic data. Experimental results on the public test set of 2023 AI City Challenge Track 1 demonstrate the efficacy of the proposed approach, achieving an IDF1 of 94.17% and securing 2nd position on the leaderboard. Codes will be available at https://***/nguyenquivinhquang/Multi-camera-People-Tracking-With-Mixture-of-Realistic-and-Synthetic-Knowledge © 2023 ieee.

关键词： Feature extraction

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A Single Residual Network with ESA Modules and Distillation

A Single Residual Network with ESA Modules and Distillation

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wang, Yucong Cai, Minjie Hunan University College of Computer Science and Electronic Engineering China

ISBN: (纸本)9798350302493

Although there are many methods based on deep learning that have superior performance on single image super-resolution (SISR), it is difficult to run in real time on devices with limited computing power. Some recent studies have found that simply relying on reducing parameters or reducing the theoretical FLOPs of the model does not speed up the inference time of the network in a practical sense. Actual speed on the device is probably a better measure than FLOPs. In this work, we propose a new single residual network (SRN). On the one hand, we try to introduce and optimize an attention mechanism module to improve the performance of the network with a relatively small speed loss. On the other hand, we find that residuals in residual blocks do not have a positive impact on networks with adjusted ESA. Therefore, the residual of the network residual block is removed, which not only improves the speed of the network, but also improves the performance of the network. Finally, we reduced the number of channels and the number of residual blocks of the classic model EDSR, and removed the last convolution before the long residual. We set this tuned EDSR as the teacher model and our newly proposed SRN as the student model. Under the joint effect of the original loss and the distillation loss, the performance of the network can be improved without losing the inference time. Combining the above strategies, our proposed model runs much faster than similarly performing models. As an example, we built a Fast and Efficient Network (SRN) and its small version SRN-S, which run 30%-37% faster than the state-of-the-art EISR model: a paper champion RLFN. Furthermore, the shallow version of SRN-S achieves the second-shortest inference time as well as the second-smallest number of activations in the NTIRE2023 challenge. Code will be available at https://***/wnxbwyc/SRN. © 2023 ieee.

关键词： Deep learning

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CNT-NeRF: Carbon Nanotube Forest Depth Layer Decomposition in SEM Imagery using Generative Adversarial Networks

CNT-NeRF: Carbon Nanotube Forest Depth Layer Decomposition i...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Nguyen, Nguyen P. Surya, Ramakrishna Calyam, Prasad Palaniappan, Kannappan Maschmann, Matthew Bunyak, Filiz University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Department of Mechanical and Aerospace Engineering ColumbiaMO United States

ISBN: (纸本)9798350302493

Carbon nanotube (CNT) forests are imaged using scanning electron microscopes (SEMs) that project their multilayered 3D structure into a single 2D image. Image analytics, particularly instance segmentation is needed to quantify structural characteristics and to predict correlations between structural morphology and physical properties. The inherent complexity of individual CNT structures is further increased in CNT forests due to density of CNTs, interactions between CNTs, occlusions, and lack of 3D information to resolve correspondences when multiple CNTs from different depths appear to cross in 2D. In this paper, we propose CNT-NeRF, a generative adversarial network (GAN) for simultaneous depth layer decomposition and segmentation of CNT forests in SEM images. The proposed network is trained using a multi-layer, photo-realistic synthetic dataset obtained by transferring the style of real CNT images to physics-based simulation data. Experiments show promising depth layer decomposition and accurate CNT segmentation results not only for the front layer but also for the partially occluded middle and back layers. This achievement is a significant step towards automated, image-based CNT forest structure characterization and physical property prediction. © 2023 ieee.

关键词： Carbon nanotubes

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Learning from Synthetic Vehicles

Learning from Synthetic Vehicles

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22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Kim, Tae Soo Shim, Bohoon Peven, Michael Qiu, Weichao Yuille, Alan Hager, Gregory D. Johns Hopkins Univ 3400 N Charles St Baltimore MD 21218 USA

ISBN: (纸本)9781665458245

In this paper, we release the Simulated Articulated VEhicles Dataset (SAVED) which contains images of synthetic vehicles with moveable vehicle parts. SAVED consists of images that are much more relevant for vehicle-related pattern-recognition tasks than other popular pretraining datasets such as ImageNet. Compared to a model initialized with ImageNet weights, we show that a model pretrained using SAVED leads to much better performance when recognizing vehicle parts and orientation directly from an image. We also find that a multi-task pretraining approach using fine-grained geometric signals available in SAVED leads to significant improvements in performance. By pretraining on SAVED instead of ImageNet, we reduce the error rate of one of the state of the art vehicle orientation estimators by 51.2% when tested on real images. We release SAVED and instructions on its usage here(1).

关键词： computer vision Image recognition Error analysis conferences Multitasking Task analysis

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Win-Fail Action recognition

Win-Fail Action Recognition

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22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Parmar, Paritosh Morris, Brendan Univ British Columbia Vancouver BC Canada Univ Nevada Las Vegas NV 89154 USA

ISBN: (纸本)9781665458245

Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understand the actions. To spur progress in the direction of a more comprehensive understanding of videos, we introduce the task of win-fail action recognition - differentiating between successful and failed attempts at various activities. We introduce a first of its kind paired win-fail action understanding dataset with samples from the following domains: "General Stunts," "Internet Wins-Fails," "Trick Shots," & "Party Games." Unlike existing action recognition datasets, intra-class variation is high making the task challenging, yet feasible. Using a battery of experiments, including a novel video retrieval test, we systematically analyze the characteristics of our win-fail task/dataset, and determine its suitability to serve as a video understanding problem benchmark. While current prototypical action recognition methods work well on our task/dataset, they still leave a large gap to achieve high performance. We hope to motivate more work towards the true understanding of actions/videos. Dataset will be available from: https://***/ParitoshParmar/Win-Fail-Action-recognition.

关键词： Analytical models Pediatrics Limiting Benchmark testing Spatial databases Spatiotemporal phenomena pattern recognition

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A Confusion Matrix for Evaluating Feature Attribution Methods

A Confusion Matrix for Evaluating Feature Attribution Method...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Arias-Duart, Anna Mariotti, Ettore Garcia-Gasulla, Dario Alonso-Moral, Jose Maria Spain Spain

ISBN: (纸本)9798350302493

The increasing use of deep learning models in critical areas of computer vision and the consequent need for insights into model behaviour have led to the development of numerous feature attribution methods. However, these attributions must be both meaningful and plausible to end-users, which is not always the case. Recent research has emphasized the importance of faithfulness in attributions, as plausibility without faithfulness can result in misleading explanations and incorrect decisions. In this work., we propose a novel approach to evaluate the faithfulness of feature attribution methods by constructing an 'Attribution Confusion Matrix', which allows us to leverage a wide range of existing metrics from the traditional confusion matrix. This approach effectively introduces multiple evaluation measures for faithfulness in feature attribution methods in a unified and consistent framework. We demonstrate the effectiveness of our approach on various datasets, attribution methods, and models, emphasizing the importance of faithfulness in generating plausible and reliable explanations while also illustrating the distinct behaviour of different feature attribution methods. © 2023 ieee.

关键词： computer vision

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Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

Contactless Respiratory Rate Monitoring For ICU Patients Bas...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Liu, Zimeng Huang, Bin Lin, Chun-Liang Wu, Chieh-Liang Zhao, Changchen Chao, Wen-Cheng Wu, Yu-Cheng Zheng, Yadan Wang, Zhiru Beihang University Hangzhou Innovataion Institute China Beihang University School of Automation Science and Electrical Engineering China National Chung Hsing University College of Electrical Engineering and Computer Science Taiwan Taichung Veterans General Hospital Department of Critical Care Medicine Taiwan National Chung Hsing University College of Medicine Department of Post-Baccalaureate Medicine Taiwan National Chung Hsing University Big Data Center Taiwan Beijing University of Posts and Telecommunications Computer Science School China Beijing Foreign Studies University Graduate School of Translation and Interpretation China

ISBN: (纸本)9798350302493

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario. © 2023 ieee.

关键词： Intensive care units

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NeighborTrack: Single Object Tracking by Bipartite Matching with Neighbor Tracklets and Its Applications to Sports

NeighborTrack: Single Object Tracking by Bipartite Matching ...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Chen, Yu-Hsi Wang, Chien-Yao Yang, Cheng-Yun Chang, Hung-Shuo Lin, Youn-Long Chuang, Yung-Yu Liao, Hong-Yuan Mark Institute of Information Science Academia Sinica Taiwan Purdue University Department of Electrical Engineering United States National Tsing Hua University Department of Computer Science Taiwan National Taiwan University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350302493

We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence score predicted by the backbone SOT network to automatically derive neighbor information and then uses this information to improve the tracking results. When tracking an occluded target, its appearance features are untrustworthy. However, a general siamese network often cannot tell whether the tracked object is occluded by reading the confidence score alone, because it could be misled by neighbors with high confidence scores. Our proposed NeighborTrack takes advantage of unoccluded neighbors' information to reconfirm the tracking target and reduces false tracking when the target is occluded. For the VOT challenge dataset commonly used in short-term object tracking, we improve three famous SOT networks, Ocean, TransT, and OSTrack, by an average of 1.92% EAO and 2.11% robustness. For the mid- and long-term tracking experiments based on OSTrack, we achieve state-of-the-art 72.25% AUC on LaSOT and 75.7% AO on GOT-10K. Most of the tracking examples we have used are related to sports. © 2023 ieee.

关键词： Target tracking

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Video Analytics for Detecting Motorcyclist Helmet Rule Violations

Video Analytics for Detecting Motorcyclist Helmet Rule Viola...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Tsai, Chun-Ming Hsieh, Jun-Wei Chang, Ming-Ching He, Guan-Lin Chen, Ping-Yang Chang, Wei-Tsung Hsieh, Yi-Kuan University of Taipei Department of Computer Science Taipei10048 Taiwan National Yang Ming Chiao Tung University College of Ai and Green Energy Tainan71150 Taiwan University at Albany State University of New York Department of Computer Science NY12222 United States

ISBN: (纸本)9798350302493

The use of helmets is essential for motorcyclists' safety, but non-compliance with helmet rules remains a common issue. In this study, we extend the frontier of AI video analytic technologies for detecting violations of helmet rules among motorcyclists. Our method can handle highly challenging conditions for traditional methods, including occlusions, fast vehicle movement, shadows, large viewing angles, poor illumination and weather conditions. We adopt the widely used YOLOv7 object detector and develop a first baseline using YOLOv7-E6E. We further develop two improved versions, namely YOLOv7-CBAM and YOLOv7-SimAM that better address the challenges. Experiments are performed on the 2023 AI City Challenge Track 5 contest benchmark. Evaluation on the 100 test videos of the contest demonstrates the effectiveness of our approach. The baseline YOLOv7-E6E model trained with image size 1920 achieves 0.6112 mAP. The YOLOv7-CBAM achieves 0.6389 mAP, and YOLOv7-SimAM achieves 0.6422 mAP, where both are trained with image size 1280. These models rank sixth, fifth, and fourth on the public leaderboard, respectively, which outperforms over 36 global participating teams. The code for our models is available at: https://***/cmtsai2023/AICITY2023-Track5-DVHRM. © 2023 ieee.

关键词： Object detection

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Applying Computational Topology for Enhanced Image recognition and computer vision 2

Applying Computational Topology for Enhanced Image Recogniti...

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2nd ieee International conference on Advances in Information Technology, ICAIT 2024

作者： Siddhan, Saravanan Moorthy, A. Christy, S. Sivasankar, C. Bhaskar, K. Vijaya Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Saveetha School of Engineering SIMATS Saveetha University Thandalam Tamil Nadu Chennai India Tamilnadu Chennai India

ISBN: (纸本)9798350383867

Computational topology has consequently shorten the time taken for image recognition with good accuracy and therefore has boosted the performance of computer vision. This paper uses computational topology in different domains like society, livestock, medicine, and remote-sensing. Two primary outcomes have been revealed in the experiment: tightly-spaced pictures, thanks to topology, have increased accuracy by up to 5.0 percent compared to conventional facilitation. Considering the topology-enhanced method edge processing time, the calculation time will increase slightly, but it will have a significant performance advantage. Additionally, the edge, texture, shape, and pattern detection are contributed to the overall classifier output, and the performed method reaches the rate of accuracy of entities detection up to the prior methods. Primarily, we develop and compare the two methods with each other on the basis of accuracy, computational cost, scalability, noise resistance. The grading results show that the improved technique influences picture identification and computer vision system, making the technologies more mature and precise to be used on the real-world tasks. © 2024 ieee.

关键词： Machine vision

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