Real - time Anomaly Target Detection and Recognition in Intelligent Surveillance Systems based on SLAM

Real - time Anomaly Target Detection and Recognition in Intelligent Surveillance Systems based on SLAM

Authors

  • Han Lei Computer Science Engineering, Santa Clara University, Santa Clara, USA
  • Zhou Chen Software Engineering, Zhejiang University, Hangzhou, China
  • Peiyuan Yang Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA
  • Zuwei Shui Information Studies, Trine University, Phoenix, USA
  • Baoming Wang Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA

DOI:

https://doi.org/10.53469/wjimt.2024.07(02).07

Keywords:

Intelligent monitoring system, SLAM technology, Anomaly detection, Target recognition

Abstract

This paper introduces the application of Simultaneous Localization and Mapping (SLAM) technology in intelligent monitoring system. Traditionally, intelligent surveillance systems utilize image processing, pattern recognition, and computer vision technologies to filter out irrelevant information and automatically identify objects of interest. However, by incorporating SLAM technology into these systems, we are taking their capabilities to the next level. SLAM is a powerful tool that enables real-time spatial perception and mapping in dynamic environments. By applying SLAM technology to intelligent monitoring systems, we can improve the system's situational awareness, automate scene analysis, and realize the precise location of events. The combination of SLAM and intelligent surveillance systems enables these systems to autonomously identify and analyze critical information from surveillance video, effectively locate accident scenes, and detect anomalies with unprecedented speed and accuracy. At the same time, we also analyze the challenges and opportunities of integrating SLAM into these systems, and the direction of future research and development. Through comprehensive analysis, we aim to shed light on the transformational impact of SLAM technology on the capabilities of intelligent surveillance systems, as well as on the ability to drive real-time, round-the-clock surveillance.

References

Ji, H., Xu, X., Su, G., Wang, J., & Wang, Y. (2024). Utilizing Machine Learning for Precise Audience Targeting in Data Science and Targeted Advertising. Academic Journal of Science and Technology, 9(2), 215-220.

Niu, H., Li, H., Wang, J., Xu, X., & Ji, H. (2023). Enhancing computer digital signal processing through the utilization of rnn sequence algorithms. International Journal of Computer Science and Information Technology, 1(1), 60-68.

Gong, Yulu, et al. "RESEARCH ON A MULTILEVEL PRACTICAL TEACHING SYSTEM FOR THE COURSE'DIGITAL IMAGE PROCESSING." OLD AND NEW TECHNOLOGIES OF LEARNING DEVELOPMENT IN MODERN CONDITIONS (2024): 272.

Wang, Y., Bao, Q., Wang, J., Su, G., & Xu, X. (2024). Cloud Computing for Large-Scale Resource Computation and Storage in Machine Learning. Journal of Theory and Practice of Engineering Science, 4(03).

Shen, Zepeng, et al. "EDUCATIONAL INNOVATION IN THE DIGITAL AGE: THE ROLE AND IMPACT OF NLP TECHNOLOGY." OLD AND NEW TECHNOLOGIES OF LEARNING DEVELOPMENT IN MODERN CONDITIONS (2024): 281.

Wu, J., Wang, H., Ni, C., Zhang, C., & Lu, W. (2024). Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models. arXiv preprint arXiv:2402.12916.

Zhang, C., Lu, W., Ni, C., Wang, H., & Wu, J. (2024). Enhanced User Interaction in Operating Systems through Machine Learning Language Models. arXiv preprint arXiv:2403.00806.

Wang, H., Bao, Q., Shui, Z., Li, L., & Ji, H. (2024). A Novel Approach to Credit Card Security with Generative Adversarial Networks and Security Assessment.

Li, X., Zheng, H., Chen, J., Zong, Y., & Yu, L. (2024). User Interaction Interface Design and Innovation Based on Artificial Intelligence Technology. Journal of Theory and Practice of Engineering Science, 4(03), 1-8.

Duan, Shiheng, et al. "THE INNOVATIVE MODEL OF ARTIFICIAL INTELLIGENCE COMPUTER EDUCATION UNDER THE BACKGROUND OF EDUCATIONAL INNOVATION." The 2nd International scientific and practical conference “Innovations in education: prospects and challenges of today”(January 16-19, 2024) Sofia, Bulgaria. International Science Group. 2024. 389 p.. 2024.

Qian, Wenpin, et al. "NEXT-GENERATION ARTIFICIAL INTELLIGENCE INNOVATIVE APPLICATIONS OF LARGE LANGUAGE MODELS AND NEW METHODS." OLD AND NEW TECHNOLOGIES OF LEARNING DEVELOPMENT IN MODERN CONDITIONS (2024): 262.

Song, T., Li, X., Wang, B., & Han, L. (2024). Research on Intelligent Application Design Based on Artificial Intelligence and Adaptive Interface.

Qi, Y., Wang, X., Li, H., & Tian, J. (2024). Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks. arXiv preprint arXiv:2403.03165.

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm. (2024). Frontiers in Computing and Intelligent Systems, 6(3), 119-122. https://doi.org/10.54097/a5jafgdv

Chen, W., Shen, Z., Pan, Y., Tan, K., & Wang, C. (2024). Applying Machine Learning Algorithm to Optimize Personalized Education Recommendation System. Journal of Theory and Practice of Engineering Science, 4(01), 101-108.

K. Xu, X. Wang, Z. Hu and Z. Zhang, "3D Face Recognition Based on Twin Neural Network Combining Deep Map and Texture," 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China, 2019, pp. 1665-1668, doi: 10.1109/ICCT46805.2019.8947113.

Shi, Peng, Yulin Cui, Kangming Xu, Mingmei Zhang, and Lianhong Ding. 2019. "Data Consistency Theory and Case Study for Scientific Big Data" Information 10, no. 4: 137. https://doi.org/10.3390/info10040137.

Zhenghua Hu, Xianmei Wang, Kangming Xu, and Pu Dong. 2020. Real-time Target Tracking Based on PCANet-CSK Algorithm. In Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence (CSAI '19). Association for Computing Machinery, New York, NY, USA, 343–346. https://doi.org/10.1145/3374587.3374607.

Zhang, Ye, et al. "Optimizing science question ranking through model and retrieval-augmented generation." International Journal of Computer Science and Information Technology 1.1 (2023): 124-130.

Xiao, J., Chen, Y., Ou, Y., Yu, H., & Xiao, Y. (2024). Baichuan2-Sum: Instruction Finetune Baichuan2-7B Model for Dialogue Summarization. arXiv preprint arXiv:2401.15496.

Zhu, M., Zhang, Y., Gong, Y., Xing, K., Yan, X., & Song, J. (2024). Ensemble Methodology: Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble. arXiv preprint arXiv:2402.17979.

Huo, Shuning, et al. "Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research." arXiv preprint arXiv:2402.16038 (2024).

Yu, Hanyi, et al. "Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving." arXiv preprint arXiv:2402.16036 (2024).

Xiang, Yafei, et al. "Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations." arXiv preprint arXiv:2402.16035 (2024).

Zhu, Mengran, et al. "Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data." arXiv preprint arXiv:2402.09830 (2024).

Gong, Yulu, et al. "Enhancing Cybersecurity Resilience in Finance with Deep Learning for Advanced Threat Detection." arXiv preprint arXiv:2402.09820 (2024).

Sun, Guolin, et al. "Revised reinforcement learning based on anchor graph hashing for autonomous cell activation in cloud-RANs." Future Generation Computer Systems 104 (2020): 60-73.

Wu, Yichao, et al. "LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models." arXiv preprint arXiv:2403.08822 (2024).

Downloads

Published

2024-03-27
Loading...