Intelligent Video Monitoring System with the Functionality of Online Recognition of People’s Behavior and Interactions Between People

Kulbacki, Marek; Segen, Jakub; Wojciechowski, Sławomir; Wereszczyński, Kamil; Nowacki, J.; Drabik, Aldona; Wojciechowski, Konrad

doi:10.1007/978-3-319-75420-8_47

Cited by 7 publications

(3 citation statements)

References 39 publications

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“…However, such in home prepared benchmarks typically lead to severe problems in their lack of standardization and/or irreproducibility [4,5]. Limitations of manually prepared data were also observed by the authors of this paper, during work on practical implementation of the surveillance project [7].…”

Section: Introductionmentioning

confidence: 92%

Application of Crowd Simulations in the Evaluation of Tracking Algorithms

Staniszewski

Foszner

Kostorz

et al. 2020

Sensors

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Tracking and action-recognition algorithms are currently widely used in video surveillance, monitoring urban activities and in many other areas. Their development highly relies on benchmarking scenarios, which enable reliable evaluations/improvements of their efficiencies. Presently, benchmarking methods for tracking and action-recognition algorithms rely on manual annotation of video databases, prone to human errors, limited in size and time-consuming. Here, using gained experiences, an alternative benchmarking solution is presented, which employs methods and tools obtained from the computer-game domain to create simulated video data with automatic annotations. Presented approach highly outperforms existing solutions in the size of the data and variety of annotations possible to create. With proposed system, a potential user can generate a sequence of random images involving different times of day, weather conditions, and scenes for use in tracking evaluation. In the design of the proposed tool, the concept of crowd simulation is used and developed. The system is validated by comparisons to existing methods.

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Section: Introductionmentioning

confidence: 92%

Application of Crowd Simulations in the Evaluation of Tracking Algorithms

Staniszewski

Foszner

Kostorz

et al. 2020

Sensors

Self Cite

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“…Pedestrians are a vital element in traffic scenarios, and the ability to detect pedestrians quickly and accurately has increasingly become a critical research topic in the field of computer vision. Pedestrian detection plays an essential role in various practical applications, such as autonomous driving perception systems [1][2][3] and intelligent security monitoring systems [4][5][6]. Additionally, pedestrian detection serves as the foundational task for downstream tasks like pedestrian tracking [7][8][9], action recognition and prediction [10][11][12], with its accuracy directly impacting the performance of these tasks.…”

Section: Introductionmentioning

confidence: 99%

Cross-modality feature fusion for night pedestrian detection

Feng,

Luo,

et al. 2024

Front. Phys.

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Night pedestrian detection with visible image only suffers from the dilemma of high miss rate due to poor illumination conditions. Cross-modality fusion can ameliorate this dilemma by providing complementary information to each other through infrared and visible images. In this paper, we propose a cross-modal fusion framework based on YOLOv5, which is aimed at addressing the challenges of night pedestrian detection under low-light conditions. The framework employs a dual-stream architecture that processes visible images and infrared images separately. Through the Cross-Modal Feature Rectification Module (CMFRM), visible and infrared features are finely tuned on a granular level, leveraging their spatial correlations to focus on complementary information and substantially reduce uncertainty and noise from different modalities. Additionally, we have introduced a two-stage Feature Fusion Module (FFM), with the first stage introducing a cross-attention mechanism for cross-modal global reasoning, and the second stage using a mixed channel embedding to produce enhanced feature outputs. Moreover, our method involves multi-dimensional interaction, not only correcting feature maps in terms of channel and spatial dimensions but also applying cross-attention at the sequence processing level, which is critical for the effective generalization of cross-modal feature combinations. In summary, our research significantly enhances the accuracy and robustness of nighttime pedestrian detection, offering new perspectives and technical pathways for visual information processing in low-light environments.

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“…Human action recognition is an interdisciplinary research direction in the field of computer vision, involving image processing, computer vision, pattern recognition, machine learning, and artificial intelligence. With the rapid development of digital image processing technology and intelligent hardware manufacturing technology, human action recognition has wide application prospects in intelligent video monitoring [1][2][3][4], natural human computer interaction [5,6], smart home products [7][8][9], and virtual reality [10]. e popularity of human action recognition has led to several survey articles that have appeared in refs [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

Tang

Wang

et al. 2020

Mathematical Problems in Engineering

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The representation and selection of action features directly affect the recognition effect of human action recognition methods. Single feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that the existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper proposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information provided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action features with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good geometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion and has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space structure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decision-making classification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and CAD60 datasets.

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Intelligent Video Monitoring System with the Functionality of Online Recognition of People’s Behavior and Interactions Between People

Cited by 7 publications

References 39 publications

Application of Crowd Simulations in the Evaluation of Tracking Algorithms

Application of Crowd Simulations in the Evaluation of Tracking Algorithms

Cross-modality feature fusion for night pedestrian detection

Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

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